1. ICLR, 2019
Jiahe Li

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
4 networks: AlexNet, GoogLeNet, VGG16, ResNet50.

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 cue conflict images (80 per category), which allows for presentation to human observers within a single experimental session.

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

9. Overcoming the texture bias of CNNs
AlexNet: 195, VGG-16: 212, ResNet-50: 299

11. Robustness and Accuracy of Shape-based Representation

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

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