Official PyTorch Implementation of the paper "Observations on K-Image Expansion of Image-Mixing Augmentation"

Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon, Jongwon Choi.

Image-mixing augmentations (e.g., Mixup and CutMix), which typically involve mixing two images, have become the de-facto training techniques for image classification. Despite their huge success in image classification, the number of images to be mixed has not been elucidated in the literature: only the naive K-image expansion has been shown to lead to performance degradation. This study derives a new K-image mixing augmentation based on the stick-breaking process under Dirichlet prior distribution. We demonstrate the superiority of our K-image expansion augmentation over conventional two-image mixing augmentation methods through extensive experiments and analyses: 1) more robust and generalized classifiers; 2) a more desirable loss landscape shape; 3) better adversarial robustness. Moreover, we show that our probabilistic model can measure the sample-wise uncertainty and boost the efficiency for network architecture search by achieving a 7-fold reduction in the search time.

• pytorch 1.13.0
• torchvision 0.14.0
• cuda 11.7 / cudnn 8.4.1

pip install -r requirements.txt

python3 main.py --root [CIFAR100_ROOT_DIRECTORY] --alpha 0.2 --trial 5 --beta 1.0 --num_classes 100 --lr 0.25 --batch_size 64 --wd 4e-5 --mode dcutmix --model pyramidnet

python3 main.py --root [CIFAR100_ROOT_DIRECTORY] --alpha 1.0 --trial 5 --beta 0.5 --num_classes 100 --lr 0.25 --batch_size 64 --wd 4e-5 --mode dcutmix --model pyramidnet_200_240

python3 main.py --root [CIFAR10_ROOT_DIRECTORY] --alpha 0.33333333333 --trial 3 --beta 1 --num_classes 10 --lr 0.25 --batch_size 64 --wd 4e-5 --mode dcutmix --model pyramidnet_200_240

• DCutMix (K=5, alpha=0.2) / PyramidNet-110 / Top-1 error 16.95% on CIFAR100:

python3 main.py --root [CIFAR100_ROOT_DIRECTORY] --num_classes 100 --batch_size 64 --model pyramidnet --eval_ckpt_path checkpoint/dcutmix_pyramidnet110_cifar100_top1_16.95.pth

@article{jeong2021observations,
  title={Observations on k-image expansion of image-mixing augmentation for classification},
  author={Jeong, Joonhyun and Cha, Sungmin and Yoo, Youngjoon and Yun, Sangdoo and Moon, Taesup and Choi, Jongwon},
  journal={arXiv preprint arXiv:2110.04248},
  year={2021}
}

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