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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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

In-Place Activated BatchNorm for Memory-Optimized Training of DNNs

Multi-agent reinforcement learning algorithm and environment

AI创造营 ：Metaverse启动机之重构现世，结合PaddlePaddle 和 Wechaty 创造自己的聊天机器人

Code implementation of Data Efficient Stagewise Knowledge Distillation paper.

A wrapper around SageMaker ML Lineage Tracking extending ML Lineage to end-to-end ML lifecycles, including additional capabilities around Feature Store groups, queries, and other relevant artifacts.

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A PyTorch implementation of "TokenLearner: What Can 8 Learned Tokens Do for Images and Videos?"

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Code repo for "Cross-Scale Internal Graph Neural Network for Image Super-Resolution" (NeurIPS'20)

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