https://github.com/HabibSlim/DKT-for-CIL
PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)
https://github.com/HabibSlim/DKT-for-CIL
computer-vision continual-learning deep-learning
Last synced: 5 days ago
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PyTorch implementation of "Dataset Knowledge Transfer for Class-Incremental Learning Without Memory" (WACV2022)
- Host: GitHub
- URL: https://github.com/HabibSlim/DKT-for-CIL
- Owner: HabibSlim
- Created: 2021-09-14T17:47:21.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-10-21T23:19:54.000Z (over 3 years ago)
- Last Synced: 2025-04-11T20:08:31.323Z (about 1 month ago)
- Topics: computer-vision, continual-learning, deep-learning
- Language: Python
- Homepage:
- Size: 40.6 MB
- Stars: 5
- Watchers: 1
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
Dataset Knowledge Transfer for Class-Incremental Learning Without Memory
[](https://www.python.org/)
[](https://pytorch.org/)
\[[Paper](https://arxiv.org/abs/2110.08421)\] \[[Slides](http://todo.todo)\]
## Summary
* [Introduction](#introduction)
* [Installation](#installation)
* [Reproducing results](#reproducing-results)
* [Citation](#citation)
* [Acknowledgments](#acknowledgments)
## Introduction
This repository contains the PyTorch implementation for the [WACV 2022](https://wacv2022.thecvf.com/) Paper ["Dataset Knowledge Transfer for Class-Incremental Learning Without Memory"](https://arxiv.org/abs/2110.08421).
This paper is the result of an internship research project conducted at [CEA-LIST](http://www-list.cea.fr/en/). Our work tackles bias-correction in incrementally learned models when no rehearsal memory is available (check out the [paper](https://arxiv.org/abs/2110.08421) for more info!).
Feel free to open a GitHub issue if you encounter any problem with the code, or if you have any question about the paper.
## Installation
Install the environments required to run the project with the following commands:
```bash
conda env create -f ./envs/FACIL.yml
conda env create -f ./envs/iCaRL.yml
```
For more details, read the ./envs/README.md file.
You will also need to extract image lists if you want to reuse the datasets presented in our paper. In order to do that, you can check the ./datasets/README.md file.
## Reproducing results
To reproduce results reported in our paper, we pre-extracted output scores on top of __LUCIR__ and __LwF__ and provide them in this repository for __CIFAR-100__ and __S=10__. Run the following command:
```bash
source ./scripts/run_eval.sh {lucir or lwf}
```
For instructions on how to train and run on top of all other methods/datasets, please read the ./scripts/README.md file.
## Citation
If our paper is helpful for your work, please consider citing it!
```bibtex
@inproceedings{slim2022_transil,
author = {Slim, Habib and Belouadah, Eden and Popescu, Adrian and Onchis, Darian},
title = {Dataset Knowledge Transfer for Class-Incremental Learning Without Memory},
booktitle = {The IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
year = {2022}
}
```
## Acknowledgments
This repository uses source code from repositories listed below:
* [FACIL Framework](https://github.com/mmasana/FACIL)
* [LUCIR](https://github.com/hshustc/CVPR19_Incremental_Learning)
* [iCaRL (LwF)](https://github.com/srebuffi/iCaRL)