Visualizing and Understanding

what’s going on in CNN

• First layer:weights, Filter, visual layers
  
  • because when input similar to weights, result will be maximized
• higher layer filters:
  
  • meaning less
• Last layer: NN in feature space
  
  • distance near, semantic similar
  • In loss, we didn’t contrict about space relation

Occusion (mask)

使用mask去看哪块对输出概率影响最大

Saliency Maps

which pixels matter for classification

output features

• compute gradient of (unnormalized) class score with respect to image image pixels, take absolute value and max over RGB channels
• segmentation without supervision
  
  • K. Simonyan, A. Vedaldi, and A. Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. International Conference on Learning Representations Workshop, 2014.
  • using grabcut on saliency maps

intermediate features

• compute gradient of neuron value with respect to image pixels
• images come out nicer if you only backprop positive gradients through each ReLU(guided backprop)

Gradient ascent

• fixed weights, tuning image pixels

Conclusion

Todos:

1. read Grabcut