This is the official implementation of the method and main experiments from the paper "Prediction Error-based Classification for Class-Incremental Learning" by Michał Zając, Tinne Tuytelaars, and Gido M. van de Ven.
The codebase is heavily based on the mammoth repository. Some of the code is also based on the class-incremental-learning repository.
The code uses Python 3, PyTorch, and packages listed in requirements.txt. If you use pip, you can install them with pip install -r requirements.txt. We include the exact package versions that we tested to work in requirements_exact_versions.txt, although these exact versions are not required.
In order to run a single experiment, you need to use main.py script, and specify arguments such as dataset, method name, etc. General arguments are defined in utils/args.py and method-specific arguments are defined in a file in models/ corresponding to a given method.
Example MNIST run for PEC method:
python3 main.py --dataset=seq-mnist --model=pec --n_epochs=1 \
--classes_per_task=1 --optim_scheduler=linear --eval_every_n_task=10 \
--force_no_augmentations=True --batch_size=1 --lr=0.01 \
--pec_architecture=mlp --pec_activation=gelu \
--pec_teacher_width_multiplier=500 --pec_width=10 --pec_output_dim=99
After running, logs will be saved to a file in results/ directory, as well as to a wandb run (a link will be displayed).
We prepared bash scripts to reproduce results from Table 1 and Table 2 under the scripts/ directory. Each script corresponds to a single result from one of the tables. For example, to reproduce PEC results on CIFAR-10 from Table 1, run
scripts/table1/cifar10/pec.sh 10
The only argument in the script is the number of seeds to run (we used 10 in the paper).
If you found our codebase useful, please consider citing our paper:
@inproceedings{
zajac2024prediction,
title={Prediction Error-based Classification for Class-Incremental Learning},
author={Micha{\l} Zaj{\k{a}}c and Tinne Tuytelaars and Gido M van de Ven},
booktitle={The Twelfth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=DJZDgMOLXQ}
}