Visualizing and UnderstandingConvolutional Networks

Matthew D. Zeiler, Rob Fergus

Presented by Huan Jin

Overview

• What are the models learning?

• Which part of the model is key to performance?

• Do the features generalize?

Convolutional Neural Network

Visualization with a Deconvnet• Unpooling:

• Max-pooling: non-invertible.• Switch: record the location of the maxima within each pooling region.

• Rectification: relu.• Filtering: apply transposed version of the filter in convnet to the rectified maps.

Convnet Visualization• Feature Visualization

Layer2: corners and other edge/color conjunctions.

Layer3: more complex invariances, capturing similar textures. (mesh patterns, text)

Convnet Visualization• Feature Visualization

Layer4: significant variation, class-specific.

Layer5: entire objects with significant pose variations.

Convnet Visualization• Feature Evolution during Training

The lower layers of the model converge within a few epochs;The upper layers develop after a considerable number of epochs (40-50).Let the model train until fully converge.

1 2 5 10 20 30 40 64 1 2 5 10 20 30 40 64 1 2 5 10 20 30 40 64 1 2 5 10 20 30 40 64

Convnet Visualization• Feature Invariance

Translation (Horizontal)

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r 7Dramatic effect Lesser impact

Convnet Visualization• Feature Invariance

Scale Invariance

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Convnet Visualization• Feature Invariance

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Rotation Variance

Convnet Visualization• Occlusion Sensitivity

• Identifying the location of the object rather than using the surrounding context.

Convnet Visualization• Occlusion Sensitivity

The strongest feature doesn’t correspond to the class label. – Multiple feature maps!

Convnet Visualization• Correspondence Analysis

• 𝜖𝜖𝑖𝑖𝑙𝑙 = 𝑥𝑥𝑖𝑖𝑙𝑙 − �𝑥𝑥𝑖𝑖𝑙𝑙 ,• 𝑥𝑥𝑖𝑖𝑙𝑙𝑎𝑎𝑎𝑎𝑎𝑎 �𝑥𝑥𝑖𝑖𝑙𝑙 𝑎𝑎𝑎𝑎𝑎𝑎 𝑡𝑡𝑡𝑎𝑎 𝑓𝑓𝑎𝑎𝑎𝑎𝑡𝑡𝑓𝑓𝑎𝑎𝑎𝑎 𝑣𝑣𝑎𝑎𝑣𝑣𝑡𝑡𝑣𝑣𝑎𝑎𝑣𝑣 𝑎𝑎𝑡𝑡 𝑙𝑙𝑎𝑎𝑙𝑙𝑎𝑎𝑎𝑎 𝑙𝑙 𝑓𝑓𝑣𝑣𝑎𝑎 𝑡𝑡𝑡𝑎𝑎 𝑣𝑣𝑎𝑎𝑜𝑜𝑜𝑜𝑜𝑜𝑎𝑎𝑎𝑎𝑙𝑙 𝑎𝑎𝑎𝑎𝑎𝑎 𝑣𝑣𝑣𝑣𝑣𝑣𝑙𝑙𝑓𝑓𝑎𝑎𝑎𝑎𝑎𝑎 𝑜𝑜𝑖𝑖𝑎𝑎𝑜𝑜𝑎𝑎𝑣𝑣.

• ∆𝑙𝑙= ∑𝑖𝑖,𝑗𝑗=1,𝑖𝑖≠𝑗𝑗5 ℋ(𝑣𝑣𝑜𝑜𝑜𝑜𝑎𝑎 𝜖𝜖𝑖𝑖𝑙𝑙 , 𝑣𝑣𝑜𝑜𝑜𝑜𝑎𝑎(𝜖𝜖𝑗𝑗𝑙𝑙))

• The lower ∆, the greater consistency.

Layer 5 implicitly establishes some form of correspondence of parts.

Layer 7 tries to discriminate between the different breeds of dog.

Convnet Visualization• Architecture Selection

Block ArtifactsToo specific low-level

Dead filters

Smaller Filters: 7*7 Smaller Stride: 2Too simple mid-level

ImageNet 2012 Revisit

ImageNet 2012 Revisit

The overall depth of the model matters!

Improve performance

Overfitting

Feature Generalization

Caltech-101

Caltech-256

• Keep layers 1-7 of the ImageNet-trained model fixed, and a newsoftmax classifier on top.

Feature GeneralizationCaltech-256

Feature GeneralizationPASCAL 2012

Only win on 5 classes. The PASCAL and ImageNet images are quite different.

Feature Analysis

As the feature hierarchies become deeper, the model can learn increasingly powerful features.

Summary

• Deconvolutional networks in visualization.• Application to convolutional neural networks

• Better understanding of what is learned• Gives insight into model construction