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<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
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<title>Awesome Transfer Learning — salad 0.2.0-alpha documentation</title>
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<li class="toctree-l1"><a class="reference internal" href="README.html">Package Overview</a><ul>
<li class="toctree-l2"><a class="reference internal" href="README.html#benchmarking-results">📊 Benchmarking Results</a></li>
<li class="toctree-l2"><a class="reference internal" href="README.html#installation">💻 Installation</a></li>
<li class="toctree-l2"><a class="reference internal" href="README.html#using-this-library">📚 Using this library</a><ul>
<li class="toctree-l3"><a class="reference internal" href="README.html#quick-start">Quick Start</a></li>
<li class="toctree-l3"><a class="reference internal" href="README.html#reasons-for-using-solver-abstractions">Reasons for using solver abstractions</a></li>
<li class="toctree-l3"><a class="reference internal" href="README.html#quickstart-mnist-experiment">Quickstart: MNIST Experiment</a></li>
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<li class="toctree-l2"><a class="reference internal" href="README.html#domain-adaptation-problems">💡 Domain Adaptation Problems</a><ul>
<li class="toctree-l3"><a class="reference internal" href="README.html#vision">📷 Vision</a></li>
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<li class="toctree-l4"><a class="reference internal" href="reading.html#multi-task-learning">Multi-task learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#policy-transfer-for-rl">Policy transfer for RL</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#few-shot-transfer-learning">Few-shot transfer learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#meta-transfer-learning">Meta transfer learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#applications">Applications</a></li>
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<li class="toctree-l3"><a class="reference internal" href="reading.html#unsupervised-domain-adaptation">Unsupervised Domain Adaptation</a><ul>
<li class="toctree-l4"><a class="reference internal" href="reading.html#theory">Theory</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#adversarial-methods">Adversarial methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#optimal-transport">Optimal Transport</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#embedding-methods">Embedding methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#kernel-methods">Kernel methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#autoencoder-approach">Autoencoder approach</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#subspace-learning">Subspace Learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#self-ensembling-methods">Self-Ensembling methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#other">Other</a></li>
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<li class="toctree-l3"><a class="reference internal" href="reading.html#semi-supervised-domain-adaptation">Semi-supervised Domain Adaptation</a><ul>
<li class="toctree-l4"><a class="reference internal" href="reading.html#general-methods">General methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#id6">Subspace learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#copulas-methods">Copulas methods</a></li>
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<li class="toctree-l3"><a class="reference internal" href="reading.html#few-shot-supervised-domain-adaptation">Few-shot Supervised Domain Adaptation</a><ul>
<li class="toctree-l4"><a class="reference internal" href="reading.html#id7">Adversarial methods</a></li>
<li class="toctree-l4"><a class="reference internal" href="reading.html#id8">Embedding methods</a></li>
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<li class="toctree-l3"><a class="reference internal" href="reading.html#applied-domain-adaptation">Applied Domain Adaptation</a><ul>
<li class="toctree-l4"><a class="reference internal" href="reading.html#physics">Physics</a></li>
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<li class="toctree-l2"><a class="reference internal" href="reading.html#datasets">Datasets</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="reading.html#text-to-text">Text-to-text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="reading.html#results">Results</a><ul>
<li class="toctree-l3"><a class="reference internal" href="reading.html#digits-transfer-unsupervised">Digits transfer (unsupervised)</a></li>
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<li class="toctree-l2"><a class="reference internal" href="reading.html#challenges">Challenges</a></li>
<li class="toctree-l2 current"><a class="reference internal" href="reading.html#libraries">Libraries</a><ul class="current">
<li class="toctree-l3 current"><a class="current reference internal" href="#">Awesome Transfer Learning</a></li>
<li class="toctree-l3"><a class="reference internal" href="#table-of-contents">Table of Contents</a></li>
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<li class="toctree-l4"><a class="reference internal" href="#surveys">Surveys</a></li>
<li class="toctree-l4"><a class="reference internal" href="#deep-transfer-learning">Deep Transfer Learning</a></li>
<li class="toctree-l4"><a class="reference internal" href="#unsupervised-domain-adaptation">Unsupervised Domain Adaptation</a></li>
<li class="toctree-l4"><a class="reference internal" href="#semi-supervised-domain-adaptation">Semi-supervised Domain Adaptation</a></li>
<li class="toctree-l4"><a class="reference internal" href="#few-shot-supervised-domain-adaptation">Few-shot Supervised Domain Adaptation</a></li>
<li class="toctree-l4"><a class="reference internal" href="#applied-domain-adaptation">Applied Domain Adaptation</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#datasets">Datasets</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#image-to-image">Image-to-image</a></li>
<li class="toctree-l4"><a class="reference internal" href="#text-to-text">Text-to-text</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="#results">Results</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#digits-transfer-unsupervised">Digits transfer (unsupervised)</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="#challenges">Challenges</a></li>
<li class="toctree-l3"><a class="reference internal" href="#libraries">Libraries</a></li>
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</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="benchmarks.html">Benchmarks</a><ul>
<li class="toctree-l2"><a class="reference internal" href="benchmarks.html#digit-benchmarks">Digit Benchmarks</a></li>
<li class="toctree-l2"><a class="reference internal" href="benchmarks.html#visda-benchmark-and-task-cv">VisDA Benchmark and TASK-CV</a></li>
</ul>
</li>
</ul>
<p class="caption"><span class="caption-text">Tutorials</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="demos/salad.solver.html">Solvers (salad.solver)</a></li>
<li class="toctree-l1"><a class="reference internal" href="demos/salad.datasets.html">Datasets (salad.datasets)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.datasets.html#introduction">Introduction</a></li>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.datasets.html#digits-datasets">Digits Datasets</a></li>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.datasets.html#toy-datasets">Toy Datasets</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="demos/salad.models.html">Models (salad.models)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.models.html#introduction">Introduction</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="demos/salad.layers.html">Layers (salad.layers)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.layers.html#introduction">Introduction</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="demos/salad.utils.html">Utilities (salad.utils)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="demos/salad.utils.html#introduction">Introduction</a></li>
</ul>
</li>
</ul>
<p class="caption"><span class="caption-text">Scripts and Paper Implementations</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="dummy.html">Domain Adversarial Training</a></li>
<li class="toctree-l1"><a class="reference internal" href="dummy.html">Cross Gradient Training</a></li>
<li class="toctree-l1"><a class="reference internal" href="dummy.html">Adversarial Dropout Regularization</a></li>
<li class="toctree-l1"><a class="reference internal" href="dummy.html">Virtual Adversarial Domain Adaptation</a></li>
<li class="toctree-l1"><a class="reference internal" href="dummy.html">Self-Ensembling</a></li>
</ul>
<p class="caption"><span class="caption-text">API Reference</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="api/salad.solver.html">Solvers (salad.solver)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.base">salad.solver.base module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.base">salad.solver.da.base module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.advdrop">salad.solver.da.advdrop module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.association">salad.solver.da.association module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.coral">salad.solver.da.coral module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.crossgrad">salad.solver.da.crossgrad module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.dann">salad.solver.da.dann module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.dirtt">salad.solver.da.dirtt module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.dirtt_re">salad.solver.da.dirtt_re module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.djdot">salad.solver.da.djdot module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.da.ensembling">salad.solver.da.ensembling module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.classification">salad.solver.classification module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.gan">salad.solver.gan module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.solver.html#module-salad.solver.openset">salad.solver.openset module</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="api/salad.datasets.html">Datasets (salad.datasets)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="api/salad.datasets.html#subpackages">Subpackages</a><ul>
<li class="toctree-l3"><a class="reference internal" href="api/salad.datasets.da.html">salad.datasets.da package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.da.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.da.html#module-salad.datasets.da.base">salad.datasets.da.base module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.da.html#module-salad.datasets.da.digits">salad.datasets.da.digits module</a></li>
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</li>
<li class="toctree-l3"><a class="reference internal" href="api/salad.datasets.digits.html">salad.datasets.digits package</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.digits.html#module-salad.datasets.digits.base">salad.datasets.digits.base module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.digits.html#module-salad.datasets.digits.mnist">salad.datasets.digits.mnist module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.digits.html#module-salad.datasets.digits.openset">salad.datasets.digits.openset module</a></li>
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<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.digits.html#module-salad.datasets.digits.usps">salad.datasets.digits.usps module</a></li>
</ul>
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<li class="toctree-l3"><a class="reference internal" href="api/salad.datasets.transforms.html">salad.datasets.transforms package</a><ul>
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<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.transforms.html#module-salad.datasets.transforms.digits">salad.datasets.transforms.digits module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.transforms.html#module-salad.datasets.transforms.ensembling">salad.datasets.transforms.ensembling module</a></li>
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</ul>
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<li class="toctree-l3"><a class="reference internal" href="api/salad.datasets.visda.html">salad.datasets.visda package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.visda.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.visda.html#module-salad.datasets.visda.detection">salad.datasets.visda.detection module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.visda.html#module-salad.datasets.visda.openset">salad.datasets.visda.openset module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.visda.html#module-salad.datasets.visda.utils">salad.datasets.visda.utils module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.datasets.visda.html#module-salad.datasets.visda.visda">salad.datasets.visda.visda module</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="api/salad.models.html">Models (salad.models)</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="api/salad.models.audio.html">salad.models.audio package</a></li>
<li class="toctree-l3"><a class="reference internal" href="api/salad.models.digits.html">salad.models.digits package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#module-salad.models.digits.adv">salad.models.digits.adv module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#module-salad.models.digits.assoc">salad.models.digits.assoc module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#module-salad.models.digits.corr">salad.models.digits.corr module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#module-salad.models.digits.dirtt">salad.models.digits.dirtt module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#module-salad.models.digits.ensemble">salad.models.digits.ensemble module</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.digits.html#salad-models-digits-fan-module">salad.models.digits.fan module</a></li>
</ul>
</li>
<li class="toctree-l3"><a class="reference internal" href="api/salad.models.vision.html">salad.models.vision package</a><ul>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.vision.html#submodules">Submodules</a></li>
<li class="toctree-l4"><a class="reference internal" href="api/salad.models.vision.html#module-salad.models.vision.unet">salad.models.vision.unet module</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#module-salad.models.base">salad.models.base module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#module-salad.models.gan">salad.models.gan module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#salad-models-neural-module">salad.models.neural module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#salad-models-resnet-module">salad.models.resnet module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#salad-models-sensorimotor-module">salad.models.sensorimotor module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#module-salad.models.transfer">salad.models.transfer module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.models.html#module-salad.models.utils">salad.models.utils module</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="api/salad.layers.html">Layers (salad.layers)</a><ul>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#submodules">Submodules</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.association">salad.layers.association module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.base">salad.layers.base module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.coral">salad.layers.coral module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.da">salad.layers.da module</a></li>
<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.funcs">salad.layers.funcs module</a></li>
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<li class="toctree-l2"><a class="reference internal" href="api/salad.layers.html#module-salad.layers.vat">salad.layers.vat module</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="api/salad.utils.html">Utilities (salad.utils)</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="api/salad.utils.html#module-salad.utils.augment">salad.utils.augment module</a></li>
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<div class="section" id="awesome-transfer-learning">
<h1>Awesome Transfer Learning<a class="headerlink" href="#awesome-transfer-learning" title="Permalink to this headline">¶</a></h1>
<p>A list of awesome papers and cool resources on transfer learning, domain
adaptation and domain-to-domain translation in general! As you will
notice, this list is currently mostly focused on domain adaptation (DA)
and domain-to-domain translation, but don’t hesitate to suggest
resources in other subfields of transfer learning. I accept pull
requests.</p>
</div>
<div class="section" id="table-of-contents">
<h1>Table of Contents<a class="headerlink" href="#table-of-contents" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><a class="reference external" href="#tutorials-and-blogs">Tutorial and Blogs</a></li>
<li><a class="reference external" href="#papers">Papers</a></li>
<li><a class="reference external" href="#surveys">Surveys</a></li>
<li><a class="reference external" href="#deep-transfer-learning">Deep Transfer Learning</a><ul>
<li><a class="reference external" href="#fine-tuning-approach">Fine-tuning approach</a></li>
<li><a class="reference external" href="#feature-extraction-embedding-approach">Feature extraction (embedding)
approach</a></li>
<li><a class="reference external" href="#policy-transfer-for-rl">Policy transfer for RL</a></li>
<li><a class="reference external" href="#few-shot-transfer-learning">Few-shot transfer learning</a></li>
<li><a class="reference external" href="#meta-transfer-learning">Meta transfer learning</a></li>
<li><a class="reference external" href="#applications">Applications</a></li>
</ul>
</li>
<li><a class="reference external" href="#unsupervised-domain-adaptation">Unsupervised Domain Adaptation</a><ul>
<li><a class="reference external" href="#theory">Theory</a></li>
<li><a class="reference external" href="#adversarial-methods">Adversarial methods</a></li>
<li><a class="reference external" href="#optimal-transport">Optimal Transport</a></li>
<li><a class="reference external" href="#embedding-methods">Embedding methods</a></li>
<li><a class="reference external" href="#kernel-methods">Kernel methods</a></li>
<li><a class="reference external" href="#autoencoder-approach">Autoencoder approach</a></li>
<li><a class="reference external" href="#subspace-learning">Subspace learning</a></li>
<li><a class="reference external" href="#self-ensembling-methods">Self-ensembling methods</a></li>
<li><a class="reference external" href="#other">Other</a></li>
</ul>
</li>
<li><a class="reference external" href="#semi-supervised-domain-adaptation">Semi-supervised Domain
Adaptation</a><ul>
<li><a class="reference external" href="#general-methods">General methods</a></li>
<li><a class="reference external" href="#subspace-learning">Subspace learning</a></li>
<li><a class="reference external" href="#copulas-methods">Copulas methods</a></li>
</ul>
</li>
<li><a class="reference external" href="#few-shot-supervised-domain-adaptation">Few-shot Supervised Domain
Adaptation</a><ul>
<li><a class="reference external" href="#adversarial-methods">Adversarial methods</a></li>
<li><a class="reference external" href="#embedding-methods">Embedding methods</a></li>
</ul>
</li>
<li><a class="reference external" href="#applied-domain-adaptation">Applied Domain Adaptation</a><ul>
<li><a class="reference external" href="#physics">Physics</a></li>
</ul>
</li>
<li><a class="reference external" href="#datasets">Datasets</a></li>
<li><a class="reference external" href="#image-to-image">Image-to-image</a></li>
<li><a class="reference external" href="#text-to-text">Text-to-text</a></li>
<li><a class="reference external" href="#results">Results</a></li>
<li><a class="reference external" href="digits-transfer">Digits transfer</a></li>
<li><a class="reference external" href="#challenges">Challenges</a></li>
<li><a class="reference external" href="#libraries">Libraries</a></li>
</ul>
</div>
<div class="section" id="tutorials-and-blogs">
<h1>Tutorials and Blogs<a class="headerlink" href="#tutorials-and-blogs" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><a class="reference external" href="http://ruder.io/transfer-learning/index.html">Transfer Learning − Machine Learning’s Next
Frontier</a></li>
<li><a class="reference external" href="https://artix41.github.io/static/domain-adaptation-in-2017/">A Little Review of Domain Adaptation in
2017</a></li>
</ul>
</div>
<div class="section" id="papers">
<h1>Papers<a class="headerlink" href="#papers" title="Permalink to this headline">¶</a></h1>
<p>Papers are ordered by theme and inside each theme by publication date
(submission date for arXiv papers). If the network or algorithm is given
a name in a paper, this one is written in bold before the paper’s name.</p>
<div class="section" id="surveys">
<h2>Surveys<a class="headerlink" href="#surveys" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference external" href="https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf">A Survey on Transfer
Learning</a>
(2009)</li>
<li><a class="reference external" href="http://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf">Transfer Learning for Reinforcement Learning Domains: A
Survey</a>
(2009)</li>
<li><a class="reference external" href="https://link.springer.com/article/10.1186/s40537-016-0043-6">A Survey of transfer
learning</a>
(2016)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1702.05374.pdf">Domain Adaptation for Visual Applications: A Comprehensive
Survey</a> (2017)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1802.03601.pdf">Deep Visual Domain Adaptation: A
Survey</a> (2018)</li>
</ul>
</div>
<div class="section" id="deep-transfer-learning">
<h2>Deep Transfer Learning<a class="headerlink" href="#deep-transfer-learning" title="Permalink to this headline">¶</a></h2>
<p>Transfer of deep learning models.</p>
<div class="section" id="fine-tuning-approach">
<h3>Fine-tuning approach<a class="headerlink" href="#fine-tuning-approach" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/pdf/1805.08974.pdf">Do Better ImageNet Models Transfer
Better?</a> (2018)</li>
</ul>
</div>
<div class="section" id="feature-extraction-embedding-approach">
<h3>Feature extraction (embedding) approach<a class="headerlink" href="#feature-extraction-embedding-approach" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://www.cv-foundation.org//openaccess/content_cvpr_workshops_2014/W15/papers/Razavian_CNN_Features_Off-the-Shelf_2014_CVPR_paper.pdf">CNN Features off-the-shelf: an Astounding Baseline for
Recognition</a>
(2014)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1804.08328v1.pdf">Taskonomy: Disentangling Task Transfer
Learning</a> (2018)</li>
</ul>
</div>
<div class="section" id="multi-task-learning">
<h3>Multi-task learning<a class="headerlink" href="#multi-task-learning" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/pdf/1606.09282">Learning without forgetting</a>
(2016)</li>
</ul>
</div>
<div class="section" id="policy-transfer-for-rl">
<h3>Policy transfer for RL<a class="headerlink" href="#policy-transfer-for-rl" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://www.rug.nl/research/portal/files/19535198/MS_PACMAN_RL.pdf">Reinforcement Learning to Train Ms. Pac-Man Using Higher-order
Action-relative
Inputs</a>
(2013)</li>
</ul>
</div>
<div class="section" id="few-shot-transfer-learning">
<h3>Few-shot transfer learning<a class="headerlink" href="#few-shot-transfer-learning" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/pdf/1707.01066.pdf">Zero-Shot Transfer Learning for Event
Extraction</a> (2017)</li>
<li><a class="reference external" href="https://www.eecs.qmul.ac.uk/~sgg/papers/ZhangEtAl_CVPR2017.pdf">Learning a Deep Embedding Model for Zero-Shot
Learning</a>
(2017)</li>
</ul>
</div>
<div class="section" id="meta-transfer-learning">
<h3>Meta transfer learning<a class="headerlink" href="#meta-transfer-learning" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="http://proceedings.mlr.press/v80/wei18a/wei18a.pdf">Transfer Learning via Learning to
Transfer</a>
(2018)</li>
</ul>
</div>
<div class="section" id="applications">
<h3>Applications<a class="headerlink" href="#applications" title="Permalink to this headline">¶</a></h3>
<div class="section" id="medical-imaging">
<h4>Medical imaging:<a class="headerlink" href="#medical-imaging" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><a class="reference external" href="https://arxiv.org/pdf/1602.03409">Deep Convolutional Neural Networks forComputer-Aided Detection: CNN
Architectures, Dataset Characteristics and Transfer
Learning</a> (2016)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1706.00712.pdf">Convolutional Neural Networks for Medical Image Analysis: Full
Training or Fine Tuning?</a>
(2017)</li>
<li><a class="reference external" href="https://orbi.uliege.be/bitstreaom/2268/222511/1/mormont2018-comparison.pdf">Comparison of deep transfer learning strategies for digital
pathology</a>
(2018)</li>
</ul>
</div>
<div class="section" id="robotics">
<h4>Robotics<a class="headerlink" href="#robotics" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><a class="reference external" href="http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CNN_LOCALIZATION_2018.pdf">A Deep Convolutional Neural Network for Location Recognition and
Geometry Based
Information</a>
(2018)</li>
</ul>
</div>
</div>
</div>
<div class="section" id="unsupervised-domain-adaptation">
<h2>Unsupervised Domain Adaptation<a class="headerlink" href="#unsupervised-domain-adaptation" title="Permalink to this headline">¶</a></h2>
<p>Transfer between a source and a target domain. In unsupervised domain
adaptation, only the source domain can have labels.</p>
<div class="section" id="theory">
<h3>Theory<a class="headerlink" href="#theory" title="Permalink to this headline">¶</a></h3>
<div class="section" id="general">
<h4>General<a class="headerlink" href="#general" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><a class="reference external" href="http://www.alexkulesza.com/pubs/adapt_mlj10.pdf">A theory of learning from different
domains</a> (2010)</li>
</ul>
</div>
<div class="section" id="multi-source">
<h4>Multi-source<a class="headerlink" href="#multi-source" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><a class="reference external" href="https://papers.nips.cc/paper/3550-domain-adaptation-with-multiple-sources.pdf">Domain Adaptation with Multiple
Sources</a>
(2008)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1805.08727.pdf">Algorithms and Theory for Multiple-Source
Adaptation</a> (2018)</li>
</ul>
</div>
</div>
<div class="section" id="adversarial-methods">
<h3>Adversarial methods<a class="headerlink" href="#adversarial-methods" title="Permalink to this headline">¶</a></h3>
<div class="section" id="learning-a-latent-space">
<h4>Learning a latent space<a class="headerlink" href="#learning-a-latent-space" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><strong>DANN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1505.07818.pdf">Domain-Adversarial Training of Neural
Networks</a> (2015)</li>
<li><strong>JAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1605.06636.pdf">Deep Transfer Learning with Joint Adaptation
Networks</a> (2016)</li>
<li><strong>CoGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1606.07536.pdf">Coupled Generative Adversarial
Networks</a> (2016)</li>
<li><strong>DRCN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1607.03516.pdf">Deep Reconstruction-Classification Networks for
Unsupervised Domain
Adaptation</a> (2016)</li>
<li><strong>DSN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1608.06019.pdf">Domain Separation
Networks</a> (2016)</li>
<li><strong>ADDA</strong>: <a class="reference external" href="https://arxiv.org/pdf/1702.05464.pdf">Adaptative Discriminative Domain
Adaptation</a> (2017)</li>
<li><strong>GenToAdapt</strong>: <a class="reference external" href="https://arxiv.org/pdf/1704.01705.pdf">Generate To Adapt: Aligning Domains using Generative
Adversarial Networks</a> (2017)</li>
<li><strong>WDGRL</strong>: <a class="reference external" href="https://arxiv.org/pdf/1707.01217.pdf">Wasserstein Distance Guided Representation Learning for
Domain Adaptation</a> (2017)</li>
<li><strong>CyCADA</strong>: <a class="reference external" href="http://proceedings.mlr.press/v80/hoffman18a/hoffman18a.pdf">CyCADA: Cycle-Consistent Adversarial Domain
Adaptation</a>
(2017)</li>
<li><strong>DIRT-T</strong>: <a class="reference external" href="https://arxiv.org/pdf/1802.08735.pdf">A DIRT-T Approach to Unsupervised Domain
Adaptation</a> (2017)</li>
<li><strong>DupGAN</strong>: <a class="reference external" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Hu_Duplex_Generative_Adversarial_CVPR_2018_paper.pdf">Duplex Generative Adversarial Network for Unsupervised
Domain
Adaptation</a>
(2018)</li>
<li><strong>MSTN</strong>: <a class="reference external" href="http://proceedings.mlr.press/v80/xie18c/xie18c.pdf">Learning Semantic Representations for Unsupervised Domain
Adaptation</a>
(2018)</li>
</ul>
</div>
<div class="section" id="image-to-image-translation">
<h4>Image-to-Image translation<a class="headerlink" href="#image-to-image-translation" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><strong>DIAT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1610.05586.pdf">Deep Identity-aware Transfer of Facial
Attributes</a> (2016)</li>
<li><strong>Pix2pix</strong>: <a class="reference external" href="https://arxiv.org/pdf/1611.07004.pdf">Image-to-Image Translation with Conditional Adversarial
Networks</a> (2016)</li>
<li><strong>DTN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1611.02200.pdf">Unsupervised Cross-domain Image
Generation</a> (2016)</li>
<li><strong>SimGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1612.07828.pdf">Learning from Simulated and Unsupervised Images through
Adversarial Training (2016)</a>
(2016)</li>
<li><strong>PixelDA</strong>: <a class="reference external" href="https://arxiv.org/pdf/1612.05424.pdf">Unsupervised Pixel–Level Domain Adaptation with
Generative Adversarial
Networks</a> (2016)</li>
<li><strong>UNIT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1703.00848.pdf">Unsupervised Image-to-Image Translation
Networks</a> (2017)</li>
<li><strong>CycleGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1703.10593">Unpaired Image-to-Image Translation using
Cycle-Consistent Adversarial
Networks</a> (2017)</li>
<li><strong>DiscoGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1703.05192.pdf">Learning to Discover Cross-Domain Relations with
Generative Adversarial
Networks</a> (2017)</li>
<li><strong>DualGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1704.02510.pdf">DualGAN: Unsupervised Dual Learning for Image-to-Image
Translation</a> (2017)</li>
<li><strong>SBADA-GAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1705.08824.pdf">From source to target and back: symmetric
bi-directional adaptive GAN</a>
(2017)</li>
<li><strong>DistanceGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1706.00826.pdf">One-Sided Unsupervised Domain
Mapping</a> (2017)</li>
<li><strong>pix2pixHD</strong>: <a class="reference external" href="https://arxiv.org/pdf/1711.11585.pdf">High-Resolution Image Synthesis and Semantic
Manipulation with Conditional
GANs</a> (2018)</li>
<li><strong>I2I</strong>: <a class="reference external" href="https://arxiv.org/pdf/1712.00479.pdf">Image to Image Translation for Domain
Adaptation</a> (2017)</li>
<li><strong>MUNIT</strong>: <a class="reference external" href="https://arxiv.org/abs/1804.04732">Multimodal Unsupervised Image-to-Image
Translation</a> (2018)</li>
</ul>
</div>
<div class="section" id="multi-source-adaptation">
<h4>Multi-source adaptation<a class="headerlink" href="#multi-source-adaptation" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><strong>StarGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1711.09020.pdf">StarGAN: Unified Generative Adversarial Networks for
Multi-Domain Image-to-Image
Translation</a> (2017)</li>
<li><strong>XGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1711.05139.pdf">XGAN: Unsupervised Image-to-Image Translation for
Many-to-Many Mappings</a>
(2017)</li>
<li><strong>BicycleGAN</strong> : <a class="reference external" href="https://arxiv.org/pdf/1711.11586.pdf">Toward Multimodal Image-to-Image
Translation</a> (2017)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1712.00123.pdf">Label Efficient Learning of Transferable Representations across
Domains and Tasks</a> (2017)</li>
<li><strong>ComboGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1712.06909.pdf">ComboGAN: Unrestrained Scalability for Image Domain
Translation</a> (2017)</li>
<li><strong>AugCGAN</strong>: <a class="reference external" href="https://arxiv.org/abs/1802.10151">Augmented CycleGAN: Learning Many-to-Many Mappings from
Unpaired Data</a> (2018)</li>
<li><strong>RadialGAN</strong>: <a class="reference external" href="https://arxiv.org/abs/1802.06403">RadialGAN: Leveraging multiple datasets to improve
target-specific predictive models using Generative Adversarial
Networks</a> (2018)</li>
<li><strong>MADA</strong>: <a class="reference external" href="https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/viewFile/17067/16644">Multi-Adversarial Domain
Adaptation</a>
(2018)</li>
<li><strong>MDAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1705.09684.pdf">Multiple Source Domain Adaptation with Adversarial
Learning</a> (2018)</li>
</ul>
</div>
<div class="section" id="temporal-models-videos">
<h4>Temporal models (videos)<a class="headerlink" href="#temporal-models-videos" title="Permalink to this headline">¶</a></h4>
<ul class="simple">
<li><strong>Model F</strong>: <a class="reference external" href="https://arxiv.org/pdf/1708.02191.pdf">Unsupervised Domain Adaptation for Face Recognition in
Unlabeled Videos</a> (2017)</li>
<li><strong>RecycleGAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1808.05174.pdf">Recycle-GAN: Unsupervised Video
Retargeting</a> (2018)</li>
<li><strong>Vid2vid</strong>: <a class="reference external" href="https://arxiv.org/pdf/1808.06601.pdf">Video-to-Video
Synthesis</a> (2018)</li>
<li><strong>Temporal Smoothing (TS)</strong>: <a class="reference external" href="https://arxiv.org/pdf/1808.07371.pdf">Everybody Dance
Now</a> (2018)</li>
</ul>
</div>
</div>
<div class="section" id="optimal-transport">
<h3>Optimal Transport<a class="headerlink" href="#optimal-transport" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>OT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1507.00504.pdf">Optimal Transport for Domain
Adaptation</a> (2015)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1610.04420.pdf">Theoretical Analysis of Domain Adaptation with Optimal
Transport</a> (2016)</li>
<li><strong>JDOT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1705.08848.pdf">Joint distribution optimal transportation for domain
adaptation</a> (2017)</li>
<li><strong>Monge map learning</strong>: <a class="reference external" href="https://arxiv.org/pdf/1711.02283.pdf">Large Scale Optimal Transport and Mapping
Estimation</a> (2017)</li>
<li><strong>JCPOT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1803.04899.pdf">Optimal Transport for Multi-source Domain Adaptation
under Target Shift</a> (2018)</li>
<li><strong>DeepJDOT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1803.10081.pdf">DeepJDOT: Deep Joint distribution optimal transport
for unsupervised domain
adaptation</a> (2018)</li>
</ul>
</div>
<div class="section" id="embedding-methods">
<h3>Embedding methods<a class="headerlink" href="#embedding-methods" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Kodirov_Unsupervised_Domain_Adaptation_ICCV_2015_paper.pdf">Unsupervised Domain Adaptation for Zero-Shot
Learning</a>
(2015)</li>
<li><strong>DAassoc</strong> : <a class="reference external" href="https://arxiv.org/pdf/1708.00938.pdf">Associative Domain
Adaptation</a> (2017)</li>
</ul>
</div>
<div class="section" id="kernel-methods">
<h3>Kernel methods<a class="headerlink" href="#kernel-methods" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>SurK</strong>: <a class="reference external" href="https://pdfs.semanticscholar.org/edb8/be020e228153163428e8b698aef1af4c5cad.pdf">Covariate Shift in Hilbert Space: A Solution via Surrogate
Kernels</a>
(2015)</li>
<li><strong>DAN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1502.02791.pdf">Learning Transferable Features with Deep Adaptation
Networks</a> (2015)</li>
<li><strong>RTN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1602.04433.pdf">Unsupervised Domain Adaptation with Residual Transfer
Networks</a> (2016)</li>
<li><strong>Easy DA</strong>: <a class="reference external" href="http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7899860">A Simple Approach for Unsupervised Domain
Adaptation</a>
(2016)</li>
</ul>
</div>
<div class="section" id="autoencoder-approach">
<h3>Autoencoder approach<a class="headerlink" href="#autoencoder-approach" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>MCAE</strong>: <a class="reference external" href="https://arxiv.org/pdf/1503.03163.pdf">Learning Classifiers from Synthetic Data Using a
Multichannel Autoencoder</a>
(2015)</li>
<li><strong>SMCAE</strong>: <a class="reference external" href="https://arxiv.org/pdf/1509.05463.pdf">Learning from Synthetic Data Using a Stacked Multichannel
Autoencoder</a> (2015)</li>
</ul>
</div>
<div class="section" id="subspace-learning">
<h3>Subspace Learning<a class="headerlink" href="#subspace-learning" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>SGF</strong>: <a class="reference external" href="https://pdfs.semanticscholar.org/d3ed/bfee56884d2b6d9aa51a6c525f9a05248802.pdf">Domain Adaptation for Object Recognition: An Unsupervised
Approach</a>
(2011)</li>
<li><strong>GFK</strong>: <a class="reference external" href="https://pdfs.semanticscholar.org/0a59/337568cbf74e7371fb543f7ca34bbc2153ac.pdf">Geodesic Flow Kernel for Unsupervised Domain
Adaptation</a>
(2012)</li>
<li><strong>SA</strong>: <a class="reference external" href="https://pdfs.semanticscholar.org/51a4/d658c93c5169eef7568d3d1cf53e8e495087.pdf">Unsupervised Visual Domain Adaptation Using Subspace
Alignment</a>
(2015)</li>
<li><strong>CORAL</strong>: <a class="reference external" href="https://arxiv.org/pdf/1511.05547.pdf">Return of Frustratingly Easy Domain
Adaptation</a> (2015)</li>
<li><strong>Deep CORAL</strong>: <a class="reference external" href="https://arxiv.org/pdf/1607.01719.pdf">Deep CORAL: Correlation Alignment for Deep Domain
Adaptation</a> (2016)</li>
<li><strong>ILS</strong>: <a class="reference external" href="https://arxiv.org/pdf/1611.08350.pdf">Learning an Invariant Hilbert Space for Domain
Adaptation</a> (2016)</li>
<li><strong>Log D-CORAL</strong>: <a class="reference external" href="https://arxiv.org/pdf/1705.08180.pdf">Correlation Alignment by Riemannian Metric for
Domain Adaptation</a> (2017)</li>
</ul>
</div>
<div class="section" id="self-ensembling-methods">
<h3>Self-Ensembling methods<a class="headerlink" href="#self-ensembling-methods" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>MT</strong>: <a class="reference external" href="https://arxiv.org/pdf/1706.05208.pdf">Self-ensembling for domain
adaptation</a> (2017)</li>
</ul>
</div>
<div class="section" id="other">
<h3>Other<a class="headerlink" href="#other" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://scalable.mpi-inf.mpg.de/files/2013/04/saenko_eccv_2010.pdf">Adapting Visual Category Models to New
Domains</a>
(2010)</li>
<li><strong>AdaBN</strong>: <a class="reference external" href="https://arxiv.org/pdf/1603.04779.pdf">Revisiting Batch Normalization for Practical Domain
Adaptation</a> (2016)</li>
</ul>
</div>
</div>
<div class="section" id="semi-supervised-domain-adaptation">
<h2>Semi-supervised Domain Adaptation<a class="headerlink" href="#semi-supervised-domain-adaptation" title="Permalink to this headline">¶</a></h2>
<p>All the source points are labelled, but only few target points are.</p>
<div class="section" id="general-methods">
<h3>General methods<a class="headerlink" href="#general-methods" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>da+lap-sim</strong> : <a class="reference external" href="http://jeffdonahue.com/papers/DAInstanceConstraintsCVPR2013.pdf">Semi-Supervised Domain Adaptation with Instance
Constraints</a>
(2013)</li>
</ul>
</div>
<div class="section" id="id1">
<h3>Subspace learning<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>EA++</strong>: <a class="reference external" href="https://papers.nips.cc/paper/4009-co-regularization-based-semi-supervised-domain-adaptation.pdf">Co-regularization Based Semi-supervised Domain
Adaptation</a>
(2010)</li>
<li><strong>SDASL</strong>: <a class="reference external" href="https://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Yao_Semi-Supervised_Domain_Adaptation_2015_CVPR_paper.pdf">Semi-supervised Domain Adaptation with Subspace Learning
for Visual
Recognition</a>
(2015)</li>
</ul>
</div>
<div class="section" id="copulas-methods">
<h3>Copulas methods<a class="headerlink" href="#copulas-methods" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>NPRV</strong>: <a class="reference external" href="https://papers.nips.cc/paper/4802-semi-supervised-domain-adaptation-with-non-parametric-copulas.pdf">Semi-Supervised Domain Adaptation with Non-Parametric
Copulas</a>
(2013)</li>
</ul>
</div>
</div>
<div class="section" id="few-shot-supervised-domain-adaptation">
<h2>Few-shot Supervised Domain Adaptation<a class="headerlink" href="#few-shot-supervised-domain-adaptation" title="Permalink to this headline">¶</a></h2>
<p>Only a few target examples are available, but they are labelled</p>
<div class="section" id="id2">
<h3>Adversarial methods<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>FADA</strong>: <a class="reference external" href="https://arxiv.org/pdf/1711.02536.pdf">Few-Shot Adversarial Domain
Adaptation</a> (2017)</li>
<li><strong>Augmented-Cyc</strong>: <a class="reference external" href="https://arxiv.org/pdf/1807.00374.pdf">Augmented Cyclic Adversarial Learning for Domain
Adaptation</a> (2018)</li>
</ul>
</div>
<div class="section" id="id3">
<h3>Embedding methods<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><strong>CCSA</strong>: <a class="reference external" href="https://arxiv.org/pdf/1709.10190.pdf">Unified Deep Supervised Domain Adaptation and
Generalization</a> (2017)</li>
</ul>
</div>
</div>
<div class="section" id="applied-domain-adaptation">
<h2>Applied Domain Adaptation<a class="headerlink" href="#applied-domain-adaptation" title="Permalink to this headline">¶</a></h2>
<p>Domain adaptation applied to other fields</p>
<div class="section" id="physics">
<h3>Physics<a class="headerlink" href="#physics" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="http://papers.nips.cc/paper/6699-learning-to-pivot-with-adversarial-networks.pdf">Learning to Pivot with Adversarial
Networks</a>
(2016)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1710.08382.pdf">Adversarial Domain Adaptation for Identifying Phase
Transitions</a> (2017)</li>
<li><a class="reference external" href="https://arxiv.org/abs/1710.08382">Identifying Quantum Phase Transitions with Adversarial Neural
Networks</a> (2017)</li>
<li><a class="reference external" href="https://arxiv.org/abs/1806.00419">Automated discovery of characteristic features of phase transitions
in many-body localization</a>
(2017)</li>
</ul>
</div>
<div class="section" id="audio-processing">
<h3>Audio Processing<a class="headerlink" href="#audio-processing" title="Permalink to this headline">¶</a></h3>
<ul class="simple">
<li><a class="reference external" href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6817520">Autoencoder-based Unsupervised Domain Adaptation for Speech Emotion
Recognition</a>
(2014)</li>
<li><a class="reference external" href="https://arxiv.org/pdf/1804.00644.pdf">Adversarial Teacher-Student Learning for Unsupervised Domain
Adaptation</a> (2018)</li>
</ul>
</div>
</div>
</div>
<div class="section" id="datasets">
<h1>Datasets<a class="headerlink" href="#datasets" title="Permalink to this headline">¶</a></h1>
<div class="section" id="image-to-image">
<h2>Image-to-image<a class="headerlink" href="#image-to-image" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference external" href="http://yann.lecun.com/exdb/mnist/">MNIST</a> vs
<a class="reference external" href="https://drive.google.com/file/d/0B9Z4d7lAwbnTNDdNeFlERWRGNVk/view">MNIST-M</a>
vs <a class="reference external" href="http://ufldl.stanford.edu/housenumbers/">SVHN</a> vs
<a class="reference external" href="https://drive.google.com/file/d/0B9Z4d7lAwbnTSVR1dEFSRUFxOUU/view">Synth</a>
vs
<a class="reference external" href="http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html#usps">USPS</a>:
digit images</li>
<li><a class="reference external" href="http://benchmark.ini.rub.de/?section=gtsrb&subsection=news">GTSRB</a>
vs <a class="reference external" href="http://graphics.cs.msu.ru/en/node/1337">Syn Signs</a> : traffic
sign recognition datasets, transfer between real and synthetic signs.</li>
<li><a class="reference external" href="http://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html">NYU Depth Dataset
V2</a>:
labeled paired images taken with two different cameras (normal and
depth)</li>
<li><a class="reference external" href="http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html">CelebA</a>: faces
of celebrities, offering the possibility to perform gender or hair
color translation for instance</li>
<li><a class="reference external" href="https://people.eecs.berkeley.edu/~jhoffman//domainadapt/">Office-Caltech
dataset</a>:
images of office objects from 10 common categories shared by the
Office-31 and Caltech-256 datasets. There are in total four domains:
Amazon, Webcam, DSLR and Caltech.</li>
<li><a class="reference external" href="https://www.cityscapes-dataset.com/">Cityscapes dataset</a>: street
scene photos (source) and their annoted version (target)</li>
<li><a class="reference external" href="http://www.cl.cam.ac.uk/research/rainbow/projects/unityeyes/">UnityEyes</a>
vs
<a class="reference external" href="https://www.mpi-inf.mpg.de/departments/computer-vision-and-multimodal-computing/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild-mpiigaze/">MPIIGaze</a>:
simulated vs real gaze images (eyes)</li>
<li><a class="reference external" href="https://people.eecs.berkeley.edu/~taesung_park/CycleGAN/datasets/">CycleGAN
datasets</a>:
horse2zebra, apple2orange, cezanne2photo, monet2photo, ukiyoe2photo,
vangogh2photo, summer2winter</li>
<li><a class="reference external" href="https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/">pix2pix
dataset</a>:
edges2handbags, edges2shoes, facade, maps</li>
<li><a class="reference external" href="http://www.socsci.ru.nl:8180/RaFD2/RaFD?p=main">RaFD</a>: facial
images with 8 different emotions (anger, disgust, fear, happiness,
sadness, surprise, contempt, and neutral). You can transfer a face
from one emotion to another.</li>
<li><a class="reference external" href="http://ai.bu.edu/visda-2017/#browse">VisDA 2017 classification
dataset</a>: 12 categories of
object images in 2 domains: 3D-models and real images.</li>
<li><a class="reference external" href="http://hemanthdv.org/OfficeHome-Dataset/">Office-Home dataset</a>:
images of objects in 4 domains: art, clipart, product and real-world.</li>
</ul>
</div>
<div class="section" id="text-to-text">
<h2>Text-to-text<a class="headerlink" href="#text-to-text" title="Permalink to this headline">¶</a></h2>
<ul class="simple">
<li><a class="reference external" href="https://www.cs.jhu.edu/~mdredze/datasets/sentiment/">Amazon review benchmark
dataset</a>:
sentiment analysis for four kinds (domains) of reviews: books, DVDs,
electronics, kitchen</li>
<li><a class="reference external" href="http://www.ecmlpkdd2006.org/challenge.html#download">ECML/PKDD Spam
Filtering</a>:
emails from 3 different inboxes, that can represent the 3 domains.</li>
<li><a class="reference external" href="http://qwone.com/~jason/20Newsgroups/">20 Newsgroup</a>: collection
of newsgroup documents across 6 top categories and 20 subcategories.
Subcategories can play the role of the domains, as describe in <a class="reference external" href="https://arxiv.org/pdf/1707.01217.pdf">this
article</a>.</li>
</ul>
</div>
</div>
<div class="section" id="results">
<h1>Results<a class="headerlink" href="#results" title="Permalink to this headline">¶</a></h1>
<p>The results are indicated as the prediction accuracy (in %) in the
target domain after adapting the source to the target. For the moment,
they only correspond to the results given in the original papers, so the
methodology may vary between each paper and these results must be taken
with a grain of salt.</p>
<div class="section" id="digits-transfer-unsupervised">
<h2>Digits transfer (unsupervised)<a class="headerlink" href="#digits-transfer-unsupervised" title="Permalink to this headline">¶</a></h2>
<table border="1" class="docutils">
<colgroup>
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
<col width="14%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Sour
ceTa
rget</th>
<th class="head">MNIS
TMNI
ST-M</th>
<th class="head">Synt
hSVH
N</th>
<th class="head">MNIS
TSVH
N</th>
<th class="head">SVHN
MNIS
T</th>
<th class="head">MNIS
TUSP
S</th>
<th class="head">USPS
MNIS
T</th>
</tr>