[info] Update README.

Author: Arthur Douillard

README.md

@@ -14,7 +14,7 @@ Every model must inherit `inclearn.models.base.IncrementalLearner`.
 :construction: --> Runnable but not yet reached expected results.\
 :x: --> Not yet implemented or barely working.\
 
-[1]: :white_check_mark: iCaRL\
+[1]: :construction: iCaRL\
 [2]: :construction: LwF\
 [3]: :construction: End-to-End Incremental Learning\
 
@@ -33,63 +33,12 @@ The metric used is the `average incremental accuracy`:
 > each batch of classes. If a single number is preferable, we report the average of
 > these accuracies, called average incremental accuracy.
 
-If I understood well, the accuracy at task i (computed on all seen tasks) is averaged
-with all previous accuracy. A bit weird, but doing so get me a curve very similar
-to what the papier displayed.
+~If I understood well, the accuracy at task i (computed on all seen tasks) is averaged~
+~with all previous accuracy. A bit weird, but doing so get me a curve very similar~
+~to what the papier displayed.~
 
----
+EDIT: I've plot on the curve the "average incremental accuracy" but I'm not sure
+if the authors plot this metrics or simply used it in the tables results. Thus I'm
+not sure of my results validity.
 
-# References
-
-[1] iCaRL:
-
-```
-@InProceedings{icarl,
-author = {Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H.},
-title = {iCaRL: Incremental Classifier and Representation Learning},
-booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
-month = {July},
-year = {2017}
-}
-```
-
-[2]: LwF:
-
-```
-@ARTICLE{lwf,
-author={Z. {Li} and D. {Hoiem}},
-journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
-title={Learning without Forgetting},
-year={2018},
-volume={40},
-number={12},
-pages={2935-2947},
-keywords={convolution;feature extraction;feedforward neural nets;learning (artificial intelligence);fine-tuning adaption techniques;CNN;forgetting method;convolutional neural network;vision system;feature extraction;Feature extraction;Deep learning;Training data;Neural networks;Convolutional neural networks;Knowledge engineering;Learning systems;Visual perception;Convolutional neural networks;transfer learning;multi-task learning;deep learning;visual recognition},
-doi={10.1109/TPAMI.2017.2773081},
-ISSN={0162-8828},
-month={Dec},}
-```
-
-[3]: End-to-End Incremental Learning:
-
-```
-@inproceedings{end_to_end_inc_learn,
-  TITLE = {{End-to-End Incremental Learning}},
-  AUTHOR = {Castro, Francisco M. and Mar{\'i}n-Jim{\'e}nez, Manuel J and Guil, Nicol{\'a}s and Schmid, Cordelia and Alahari, Karteek},
-  URL = {https://hal.inria.fr/hal-01849366},
-  BOOKTITLE = {{ECCV 2018 - European Conference on Computer Vision}},
-  ADDRESS = {Munich, Germany},
-  EDITOR = {Vittorio Ferrari and Martial Hebert and Cristian Sminchisescu and Yair Weiss},
-  PUBLISHER = {{Springer}},
-  SERIES = {Lecture Notes in Computer Science},
-  VOLUME = {11216},
-  PAGES = {241-257},
-  YEAR = {2018},
-  MONTH = Sep,
-  DOI = {10.1007/978-3-030-01258-8\_15},
-  KEYWORDS = {Incremental learning ; CNN ; Distillation loss ; Image classification},
-  PDF = {https://hal.inria.fr/hal-01849366/file/IncrementalLearning_ECCV2018.pdf},
-  HAL_ID = {hal-01849366},
-  HAL_VERSION = {v1},
-}
-```
+---