🗣 This repository includes the main code to reproduce our results from the following sections of the paper:

• 4.1 ImageNet Transfer
• 4.4 Depth-wise Probes and Comparison to CKA

The results shown in Sections 4.2 and 4.3 can be reproduced via the code in this repository.

pypoetry doc is very well written and detailed.

First, be sure to not be in a virtual env.

To install poetry with the right version : curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | POETRY_VERSION=1.1.6 python (on Windows, from PowerShell) (Invoke-WebRequest -Uri https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py -UseBasicParsing).Content | POETRY_VERSION=1.1.6 python -

By default, poetry will create a virtualenv in a cache-dir folder. To have it created in the repository, under a .venv folder, you need to first run poetry config virtualenvs.in-project true (https://python-poetry.org/docs/configuration/#virtualenvsin-project-boolean). Then go to our repository, and run poetry install. It will create a virtualenv that can be used in PyCharm, with all the dependencies needed.

Before running the code, you need to make sure the datasets are placed in a root data folder, the code does not download any datasets and instead will throw an error if it cannot find the datasets.

• For ImageNet we need to have the following files inside the root data folder:
  1. ILSVRC2012_img_val.tar
  2. ILSVRC2012_img_train.tar
  3. ILSVRC2012_img_test_v10102019.tar
  4. ILSVRC2012_devkit_t12.tar.gz
• For CUB we need to have a folder called CUB_200_2011 inside the root data folder containing the dataset.
• For Scenes we need to have a folder called Scenes inside the root data folder containing the dataset.

In order to run these experiments, you need to follow a two-step procedure:

1. Train a model with a CL strategy (either FineTuning, LwF, or EWC) on a sequence of tasks (scenario). The scenario has the form of task_one2task_two. So for the task sequence of ImageNet ➡ Scenes ➡ CUB, we would have the scenario of ImageNet2Scenes2CUB.
2. Step 1 will save snapshots of the model at the end of each task, we can then run a linear prob model on these snapshots.

For example, running and evaluating EWC on ImageNet2Scenes2CUB would look like:

poetry run python main.py --scenario "ImageNet2Scenes2CUB" --data_root "path_to_root_data_dir" --strategy "EWC"

Now to run the linear prob evaluation at the end of training for Scenes and CUB we need to run the followings:

poetry run python main.py --probe --probe_caller "Scenes" --scenario "ImageNet2Scenes2CUB" --data_root "path_to_root_data_dir" --model_path "../../model_zoo/EWC_ImageNet2Scenes2CUB_VGG16_scenes.pt"
poetry run python main.py --probe --probe_caller "CUB" --scenario "ImageNet2Scenes2CUB" --data_root "path_to_root_data_dir" --model_path "../../model_zoo/EWC_ImageNet2Scenes2CUB_VGG16_cub.pt"

The tensorboard results will be saved in the tb_logs directory.

@inproceedings{davari2022probing,
 title      = {Probing Representation Forgetting in Supervised and Unsupervised Continual Learning},
 author     = {Davari, MohammadReza and Asadi, Nader and Mudur, Sudhir and Aljundi, Rahaf and Belilovsky, Eugene},
 booktitle  = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)},
 year       = {2022},
 month      = {June},
 }