Texture Bias

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It is claimed in [1] [2] [3] that CNN is biased towards texture, that is, CNN tends to classify an object based on its texture intead of its shape.

In [4], it is claimed that texture-bias is caused by data augmentation approach. Using different data augmentation approaches can introduce either texture-bias or shape-bias. Similarly, [5] debiases shape and texture.

Reference

  1. Geirhos, Robert, et al. “ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness.” ICLR, 2019. [code]

  2. Brochu, Francis. “Increasing Shape Bias in ImageNet-Trained Networks Using Transfer Learning and Domain-Adversarial Methods.” arXiv preprint arXiv:1907.12892 (2019).

  3. Asadi, Nader, Mehrdad Hosseinzadeh, and Mahdi Eftekhari. “Towards Shape Biased Unsupervised Representation Learning for Domain Generalization.” arXiv preprint arXiv:1909.08245 (2019).

  4. Hermann, Katherine, Ting Chen, and Simon Kornblith. “The origins and prevalence of texture bias in convolutional neural networks.” NeurIPS, (2020).

  5. Yingwei Li, Qihang Yu, Mingxing Tan, Jieru Mei, Peng Tang, Wei Shen, Alan Yuille, Cihang Xie, “SHAPE-TEXTURE DEBIASED NEURAL NETWORK TRAINING”， ICLR, 2021.