visualizingand understanding convolutionnetworksvisualizingand understanding convolutionnetworks...
TRANSCRIPT
![Page 1: Visualizingand Understanding ConvolutionNetworksVisualizingand Understanding ConvolutionNetworks Authors: Matthew D. Zeiler, Rob Fergus New York University Presenter: Jason Ren Some](https://reader033.vdocuments.mx/reader033/viewer/2022042417/5f3363d0c0c73422333a9111/html5/thumbnails/1.jpg)
Visualizing and Understanding
Convolution Networks
Authors: Matthew D. Zeiler, Rob Fergus New York University
Presenter: Jason Ren Some sides are modified based on Hamid Izadinia’s slides
on Vision seminar on Autumn 2014
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Main Contributions
• Give insight into the internal operation &behavior
• Diagnostic & Improve the performance
• Occlusion experiments for spatial understanding
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Architecture
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Difference with Alex-Net
Small filter size & Small stride #Modified according to Visual Results
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5
Approach
• Interpret the intermediate-layer features activities
• What patch cause activation in feature map
• How?
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Deconvnet & Convnet
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Unpooling
Approximate Inverse
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• Rectification
• Relu
• Filtering
• Transposed Version
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Feature Visualization
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Feature Visualization
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Feature Visualization
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Feature Visualization
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Notes
• Hierarchical representation of features
• Larger invariance in higher layers(Layer 5)
• Selective of discriminative parts of image
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Feature Evolution During Training
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Feature Invariance
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Feature Invariance
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Small 1st layer filter & stride
• Layer 1: more coverage of middle frequencies
• Layer 2: less aliasing artifacts
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Occlusion Sensitivity
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Experiments
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Experiments - size
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Experiments - generalization
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Experiments - generalization
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Experiments - feature analysis
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Q & A
Thanks!