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!