Post on 09-Jul-2020
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
1
Main Contributions
• Give insight into the internal operation &behavior
• Diagnostic & Improve the performance
• Occlusion experiments for spatial understanding
Architecture
Difference with Alex-Net
Small filter size & Small stride #Modified according to Visual Results
5
Approach
• Interpret the intermediate-layer features activities
• What patch cause activation in feature map
• How?
Deconvnet & Convnet
6
Unpooling
Approximate Inverse
• Rectification
• Relu
• Filtering
• Transposed Version
8
Feature Visualization
Feature Visualization
Feature Visualization
Feature Visualization
Notes
• Hierarchical representation of features
• Larger invariance in higher layers(Layer 5)
• Selective of discriminative parts of image
Feature Evolution During Training
Feature Invariance
Feature Invariance
Small 1st layer filter & stride
• Layer 1: more coverage of middle frequencies
• Layer 2: less aliasing artifacts
Occlusion Sensitivity
Experiments
Experiments - size
Experiments - generalization
Experiments - generalization
Experiments - feature analysis
Q & A
Thanks!