1. Most of the code I did during my PhD is available publicly on this github account.

2. I do research in Continual Learning at DeepMind, where I aim to train large neural networks on continually growing amount of tasks. You can see my work on myGoogle Scholar page. Don't hesitate to contact me to chat about that.

3. I'm the author of a free course on Deep Learning for Computer Vision -->DeepcourseYou'll learn from the basics (backprop) to the latest research done in a wide variety of domains. Each topic has lessons, coding exercices (with hidden solutions), and Anki flashcards.

4. I'm french, of course I'm a foodie.

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1. dytoxdytoxPublic

   Dynamic Token Expansion with Continual Transformers, accepted at CVPR 2022

   Python 137 17

2. incremental_learning.pytorchincremental_learning.pytorchPublic

   A collection of incremental learning paper implementations including PODNet (ECCV20) and Ghost (CVPR-W21).

   Python 397 59

3. CVPR2021_PLOPCVPR2021_PLOPPublic

   Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation

   Python 151 22

4. Continvvm/continuumContinvvm/continuumPublic

   A clean and simple data loading library for Continual Learning

   Python 427 41

5. deepcoursedeepcoursePublic

   Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki

   Jupyter Notebook 138 18

6. google-deepmind/dm_nevisgoogle-deepmind/dm_nevisPublic

   NEVIS'22: Benchmarking the next generation of never-ending learners

   Python 101 6