TCIL

Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning

Datasets

• Training datasets

  1. CIFAR100: CIFAR100 dataset will be auto-downloaded.
  2. ImageNet100: ImageNet100 is a subset of ImageNet. You need to download ImageNet first, and split the dataset refer to ImageNet100_Split.

• Class ordering

  • We use the class ordering proposed by DER.

• Structure of data directory

  data
  ├── cifar100
  │   └── cifar-100-python
  │       ├── train
  │       ├── test
  │       ├── meta
  │       └── file.txt~
  │      
  ├── imagenet100
  │   ├── train
  │   └── val
  

Environment

You can find all the libraries in the requirements.txt, and configure the experimental environment with the following commands.

conda create -n TCIL python=3.8
conda install pytorch==1.8.1 torchvision==0.9.1 cudatoolkit=11.1 -c pytorch
pip install -r requirements.txt

Thanks for the great code base from DER.

Launching an experiment

Train

sh scripts/run.sh

Eval

sh scripts/inference.sh

Prune

sh scripts/prune.sh

Results

Rehearsal Setting

Non-Rehearsal Setting

Checkpoints

Get the trained models from BaiduNetdisk(passwd:q3eh). (We both offer the training logs in the same file)