The official PyTorch code for 'DER: Dynamically Expandable Representation for Class Incremental Learning' accepted by CVPR2021

Last update: Jan 01, 2023

Related tags

Deep Learning DER-ClassIL.pytorch

Overview

DER.ClassIL.Pytorch

This repo is the official implementation of DER: Dynamically Expandable Representation for Class Incremental Learning (CVPR 2021)

Dataset

ImageNet100 Refer to ImageNet100_Split

Training

Change to corresponding directory and run the following commands

sh scripts/run.sh

Inference

Inference command:

sh scripts/inference.sh

Tips

create a new exp folder

rsync -rv --exclude=tensorboard --exclude=logs --exclude=ckpts --exclude=__pycache__ --exclude=results --exclude=inbox ./codes/base/ ./exps/der_womask/10steps/trial0

Acknowledgement

Thanks for the great code base from https://github.com/arthurdouillard/incremental_learning.pytorch.

Citation

If you are using the DER in your research or with to refer to the baseline results published in this repo, please use the following BibTex entry.

@article{yan2021dynamically,
  title={DER: Dynamically Expandable Representation for Class Incremental Learning},
  author={Yan, Shipeng and Xie, Jiangwei and He, Xuming},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021}
}

The official PyTorch code for 'DER: Dynamically Expandable Representation for Class Incremental Learning' accepted by CVPR2021

Related tags

Overview

DER.ClassIL.Pytorch

Dataset

Training

Inference

Tips

Acknowledgement

Citation

Owner

rhyssiyan

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