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Cnn visualizing

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Visualizing what ConvNets learn
丁煜航

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Visualize the weights

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Deconvnet
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler and Rob Fergus

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Cnn visualizing

  1. 1. Visualizing what ConvNets learn 丁煜航
  2. 2. Visualize the weights
  3. 3. Deconvnet Visualizing and Understanding Convolutional Networks Matthew D. Zeiler and Rob Fergus
  4. 4. Result corners and other edge/color conjunctions
  5. 5. textures
  6. 6. class-specific difference
  7. 7. Contribution 1
  8. 8. Contribution 2
  9. 9. Units activation Feed image to networks, order them by the activation of unit of a layer(pool5) Rich feature hierarchies for accurate object detection and semantic segmentation Ross Girshick Jeff Donahue Trevor Darrell Jitendra
  10. 10. Occlude the image and show the activation of the layer5 unit
  11. 11. Learning Deep Features for Discriminative Localization Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, Antonio Torralba the convolutional units of various layers of CNNs actually behave as object detectors despite no supervision on the location of the object was provided
  12. 12. Network (GAP/softmax instead of fc)
  13. 13. discriminative regions for different categories are different

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