This repository contains the code for our ICLR2024 paper Class Incremental Learning via Likelihood Ratio Based Task Prediction by Haowei Lin, Yijia Shao, Weinan Qian, Ningxin Pan, Yiduo Guo, and Bing Liu.

Update [2024.2.10]: Now we support DER++, Non-CL, and more pre-trained visual encoders!

• Overview
• Requirements
• Training
• Extension
• Bugs or Questions?
• Acknowledgements
• Citation

First, install PyTorch by following the instructions from the official website. We run the experiments on Pytorch 2.0.1, and PyTorch version higher than 1.6.0 should also work. For example, if you use Linux and CUDA11 (how to check CUDA version), install PyTorch by the following command,

pip install torch==1.6.0+cu110 -f https://download.pytorch.org/whl/torch_stable.html

If you instead use CUDA <11 or CPU, install PyTorch by the following command,

pip install torch==1.6.0

Then run the following script to install the remaining dependencies,

pip install -r requirements.txt

Attention: Our model is based on timm==0.4.12. Using them from other versions may cause some unexpected bugs.

In the following section, we describe how to train the TPL model by using our code.

Data

Before training and evaluation, please download the datasets (CIFAR-10, CIFAR-100, TinyImageNet). The default working directory is set as ~/data in our code. You can modify it according to your need.

Pre-train Model

We use the pre-train DeiT model provided by MORE. Please download it and save the file as ./ckpt/pretrained/deit_small_patch16_224_in661.pth. If you would like to test other pre-trained visual encoders, also download to the same place (you can find the pre-trained weights in timm or huggingface). We provide the scripts for Dino, MAE, CILP, ViT (small, tiny), DeiT (small, tiny).

Training scripts

We provide the examplar training and evaluation script as deit_small_in661.sh. Just run the following command and you will get the results:

bash scripts/deit_small_in661.sh

This script performs both training and testing. The default training will train TPL for 5 random seeds. In training, the results will be logged in ckpt and the training results are $HAT_{CIL}$ without using TPLR inference techniques. After running evaluation, it will be replaced with new results. If you find you get a bad results, try to check if you run the eval.py accurately. The results for the first run with seed=2023 will be saved in ./ckpt/seq0/seed2023/progressive_main_2023.

For the results in the paper, we use Nvidia A100 GPUs with CUDA 11.7. Using different types of devices or different versions of CUDA/other software may lead to slightly different performance.

Our repo also supports running baselines like DER++. If you are interested in other baselines, just follow the same way of DER++ to integrate your new code. Also, if you want to test TIL+OOD methods, you can just modify the inference code and include the OOD score computation in baseline.py. Our code base is vey extensible.

If you have any questions related to the code or the paper, feel free to email Haowei. If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

We thank PyContinual for providing an extensible framework for continual learning. We use their code structure as a reference when developing this code base.

Please cite our paper if you use this code or part of it in your work:

@inproceedings{lin2024class,
      title={Class Incremental Learning via Likelihood Ratio Based Task Prediction}, 
      author={Haowei Lin and Yijia Shao and Weinan Qian and Ningxin Pan and Yiduo Guo and Bing Liu},
      year={2024},
      booktitle={International Conference on Learning Representations}
}