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Texture vs Shape Classification: Overcoming CNN Texture Bias

This study investigates the texture bias of Convolutional Neural Networks (CNNs) in object recognition and presents a method to overcome it using shape-based representation. Experiments are conducted using different data sets and stylization techniques, showing improved robustness and accuracy.

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Texture vs Shape Classification: Overcoming CNN Texture Bias

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Presentation Transcript

  1. Jiahe Li 2019.1.24 ICLR, 2019

  2. Outlines • Introduction • Experiments • Conclusions

  3. Introduction: Texture versus Shape Classification of a standard ResNet-50

  4. Data Sets • Original (160 natural colour images of objects (10 per category) with white background) • Greyscale • Silhouette • Edges • Texture

  5. Results

  6. Data Sets • Cue conflict • Images generated using iterative style transfer (Gatys et al., 2016) between an image of the Texture data set (as style) and an image from the Original data set (as content). We generated a total of 1280 cue conflict images (80 per category), which allows for presentation to human observers within a single experimental session.

  7. Stylized-ImageNet (SIN) • AdaIN style transfer (Huang & Belongie, 2017) • Different stylization techniques • Take prohibitively long with an iterative approach

  8. Overcoming the texture bias of CNNs

  9. Robustness and Accuracy of Shape-based Representation

  10. Robert Geirhos et al. Generalisation in humans and deep neural networks, 2018 NeurIPS

  11. Conclusions • Texture bias of CNNs trained on IN • A step towards more plausible models of human visual object recognition • Emergent benefits

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