Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper

Last update: Aug 29, 2022

Related tags

Deep Learning Continual_Learning_Filter_Atom_Swapping

Overview

Continual Learning With Filter Atom Swapping

Pytorch Implementation of Continual Learning With Filter Atom Swapping (ICLR'22 Spolight) Paper

If find the repo helpful for your work, please cite the following bib entries:

@inproceedings{Miao_2022_CLatoms,
title={Continual Learning With Filter Atom Swapping},
author={Miao, Zichen and Wang, Ze and Chen, Wei and Qiu, Qiang},
booktitle={International Conference on Learning Representations},
year={2022}}

Requirements

PyTorch >= version 1.8

Class-incremental experiments

Dataset

CIFAR100 will be automatically downloaded to 'Datasets' dir. Please download ImageNet to 'Datasets/ImageNet'

Training Scripts

cifar100 (N=5)

$ ./train_dcf_c100.sh 6 GPU_TO_USE

cifar100 (N=10)

$ ./train_dcf_c100.sh 11 GPU_TO_USE

ImageNet-Sub (N=5)

$ ./train_dcf_img100.sh 6 GPU_TO_USE

ImageNet-Sub (N=10)

$ ./train_dcf_img100.sh 11 GPU_TO_USE

Results

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71 Dec 28, 2022

Comments

Code for inter-task ensemble and task-incremental experiment

Hi, Thanks for this excellent paper. Could you upload the Code for inter-task ensemble and task-incremental experiment as Table 2? I think inter-task ensemble is one of the highlights of the article.

opened by WeiLeZhang 0

Owner

GitHub

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