Observations on K-image Expansion of Image-Mixing Augmentation for Classification
This work addresses a specific bottleneck in image classification for researchers and practitioners by improving augmentation techniques, though it is incremental in nature.
The paper tackles the problem of expanding image-mixing augmentations beyond two images, which previously degraded performance, by deriving a new K-image mixing method using a stick-breaking process under a Dirichlet prior. The result shows superiority over conventional methods, including a 7-fold reduction in network architecture search time.
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. Code will be available at https://github.com/yjyoo3312/DCutMix-PyTorch.git.