https://github.com/wangkai930418/erd
code for our paper "Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition" in CVPR 2022 3rd CLVISION continual learning workshop
https://github.com/wangkai930418/erd
continual-learning few-shot-learning incremental-learning knowledge-distillation meta-learning
Last synced: 12 months ago
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code for our paper "Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition" in CVPR 2022 3rd CLVISION continual learning workshop
- Host: GitHub
- URL: https://github.com/wangkai930418/erd
- Owner: wangkai930418
- Created: 2022-06-11T20:58:53.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-06-11T21:30:08.000Z (about 4 years ago)
- Last Synced: 2025-04-10T00:47:50.956Z (about 1 year ago)
- Topics: continual-learning, few-shot-learning, incremental-learning, knowledge-distillation, meta-learning
- Language: Python
- Homepage:
- Size: 1.99 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# ERD for IML
code for our CVPRW 2022 paper [Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition](https://openaccess.thecvf.com/content/CVPR2022W/CLVision/html/Wang_Incremental_Meta-Learning_via_Episodic_Replay_Distillation_for_Few-Shot_Image_Recognition_CVPRW_2022_paper.html) by [Kai Wang](https://wangkai930418.github.io/), [Xialei Liu](https://xialeiliu.github.io/), [Andrew D. Bagdanov](https://scholar.google.com/citations?user=_Fk4YUcAAAAJ&hl=en), [Luis Herranz](http://www.lherranz.org/), Shangling Jui, and [Joost van de Weijer](http://www.cvc.uab.es/LAMP/joost/).
Our supplementary material is also attached here as [supp.pdf](https://github.com/wangkai930418/ERD/blob/c72da697da5378026eff51c359984d1cefe359ab/supp.pdf).
## required packages
All installed packages in our running environment are in *requirements.txt*, please check whether you have any conflicts if enconterring any problem.
## 1st step: download dataset and create split
1,Pleas run
```
get_cifar_data.py
```
to download cifar dataset
from torchvision.datasets automatically and it will be
saved at *./data/cifar*.
2, Run
```
cd ./data/cifar
./data/cifar/create_cifar_split_auto.py
```
to create the 16-task cifar split.
## 2nd step: run the experiments
cd back to the root directory. Then run
```
cd ../..
reproduce.sh
```
to reproduce the CIFAR100 experiments in our paper.
*FT_cifar.py* is the finetuning baseline.
*ERD_cifar.py* is our method ERD implementation.
## 3rd step: test the experiments
Run
```
test_cifar.sh
```
to test the model performance.
By default, we print out the test accuracy and save the results in *./results/* directory.
## **REMIND ME** if you need further information and details on other datasets since I may forget to update the repositories later.
## Our bibtex is:
```
@InProceedings{Wang_2022_CVPR,
author = {Wang, Kai and Liu, Xialei and Bagdanov, Andrew D. and Herranz, Luis and Jui, Shangling and van de Weijer, Joost},
title = {Incremental Meta-Learning via Episodic Replay Distillation for Few-Shot Image Recognition},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2022},
pages = {3729-3739}
}
```