StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures

• URL: http://arxiv.org/abs/2108.10549v1
• Date: Tue, 24 Aug 2021 07:17:02 GMT
• Title: StyleAugment: Learning Texture De-biased Representations by Style Augmentation without Pre-defined Textures
• Authors: Sanghyuk Chun, Song Park
• Abstract summary: Recently powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artistic style transfer method, called Stylized ImageNet, can reduce the texture bias. However, Stylized ImageNet approach has two drawbacks in fidelity and diversity. We propose a StyleAugment by augmenting styles from the mini-batch.
• Score: 7.81768535871051
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent powerful vision classifiers are biased towards textures, while shape information is overlooked by the models. A simple attempt by augmenting training images using the artistic style transfer method, called Stylized ImageNet, can reduce the texture bias. However, Stylized ImageNet approach has two drawbacks in fidelity and diversity. First, the generated images show low image quality due to the significant semantic gap betweeen natural images and artistic paintings. Also, Stylized ImageNet training samples are pre-computed before training, resulting in showing the lack of diversity for each sample. We propose a StyleAugment by augmenting styles from the mini-batch. StyleAugment does not rely on the pre-defined style references, but generates augmented images on-the-fly by natural images in the mini-batch for the references. Hence, StyleAugment let the model observe abundant confounding cues for each image by on-the-fly the augmentation strategy, while the augmented images are more realistic than artistic style transferred images. We validate the effectiveness of StyleAugment in the ImageNet dataset with robustness benchmarks, such as texture de-biased accuracy, corruption robustness, natural adversarial samples, and occlusion robustness. StyleAugment shows better generalization performances than previous unsupervised de-biasing methods and state-of-the-art data augmentation methods in our experiments.

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