Observations on K-image Expansion of Image-Mixing Augmentation for
Classification
- URL: http://arxiv.org/abs/2110.04248v1
- Date: Fri, 8 Oct 2021 16:58:20 GMT
- Title: Observations on K-image Expansion of Image-Mixing Augmentation for
Classification
- Authors: Joonhyun Jeong, Sungmin Cha, Youngjoon Yoo, Sangdoo Yun, Taesup Moon,
and Jongwon Choi
- Abstract summary: This paper derives a new K-image mixing augmentation based on the stick-breaking process under Dirichlet prior.
We show that our method can train more robust and generalized classifiers through extensive experiments and analysis on classification accuracy, a shape of a loss landscape and adversarial robustness, than the usual two-image methods.
- Score: 33.99556142456945
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Image-mixing augmentations (e.g., Mixup or CutMix), which typically mix two
images, have become de-facto training tricks for image classification. Despite
their huge success on image classification, the number of images to mix has not
been profoundly investigated by the previous works, only showing the naive
K-image expansion leads to poor performance degradation. This paper derives a
new K-image mixing augmentation based on the stick-breaking process under
Dirichlet prior. We show that our method can train more robust and generalized
classifiers through extensive experiments and analysis on classification
accuracy, a shape of a loss landscape and adversarial robustness, than the
usual two-image methods. Furthermore, we show that our probabilistic model can
measure the sample-wise uncertainty and can boost the efficiency for Network
Architecture Search (NAS) with 7x reduced search time.
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