texture bias

**Texture Bias** is the **tendency of convolutional neural networks to classify images primarily based on local texture patterns rather than global shape** — CNNs rely on texture (surface patterns, colors, local statistics) more than shape (outlines, contours, global structure), while humans rely primarily on shape. **Texture Bias Evidence** - **Stylized ImageNet**: When texture and shape conflict (elephant shape with cat texture), CNNs classify by texture ("cat"), humans classify by shape ("elephant"). - **Conflict Experiments**: Geirhos et al. (2019) systematically showed CNNs use texture cues over shape cues. - **Robustness**: Texture-biased models are less robust to distribution shifts — textures change more than shapes across domains. - **Training**: Training on stylized images (removing texture) shifts CNNs toward shape bias and improves robustness. **Why It Matters** - **Robustness**: Shape-biased models are more robust to noise, domain shifts, and perturbations than texture-biased models. - **Alignment**: Human perception is shape-based — aligning model features with human perception improves interpretability. - **Semiconductor**: Defect classification should be based on shape/morphology, not texture artifacts from imaging conditions. **Texture Bias** is **judging by the surface** — CNNs' preference for local texture over global shape, causing brittle, non-robust classification.