This repository contains the PyTorch code for the BMVC 2023 paper "Infinite Class Mixup".
The paper is available here: https://arxiv.org/abs/2305.10293
The repository includes:
- Utilization files for mixing and loading datasets.
- ResNet with extra transformation on top for dual-axis mixup learning.
- Code for running on CIFAR and CUB Birds.
To run a baseline ResNet-32 on CIFAR-10 or CIFAR-100, run the following command:
python mixup_cifar.py -d cifar10/100 -m none
Note: the base code assumes the data is stored in "../../data/". If your folder is different, change the directory accordingly in "utils.py".
To run standard and our Mixup variants (e.g., on CIFAR-100), run one of the following commands:
python mixup_cifar.py -d 100 -m mixup -a 0.2
python mixup_cifar.py -d 100 -m icmixup-f -a 0.2
python mixup_cifar.py -d 100 -m regmixup -a 20
python mixup_cifar.py -d 100 -m regicmixup-f -a 20
To get results using only the class- or pair-axis, replace the "-f" in the ic variants with "-s" or "-c".
For CUB Birds experiments, you can run the same commands as above using the "mixup_birds.py" file. Note again that the efault data directory is "../../data/" in "utils.py".
Please cite the paper as follows:
@inproceedings{mensink2023infinite,
title={Infinite Class Mixup},
author={Mensink, Thomas and Mettes, Pascal},
booktitle={British Machine Vision Conference},
year={2023}
}