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Deconvolutional
Networks
Overview
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Motivation
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Beyond Edges?
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Two Challenges
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Recap: Sparse Coding (Patch-based)
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Talk Overview
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Talk Overview
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Single Deconvolutional Layer
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Single Deconvolutional Layer
Single Deconvolutional Layer
Single Deconvolutional Layer
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Single Deconvolutional Layer
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Single Deconvolutional Layer
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Single Deconvolutional Layer
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Top
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Dec
om
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Single Deconvolutional Layer
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Single Deconvolutional Layer
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Toy Example
Objective for Single Layer
Inference for Single Layer
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Effect of Sparsity
Local Inhibition/Explaining Away
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Deconvolutional Networks 23
Local Inhibition/Explaining Away
Image
Filters
Talk Overview
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3D Max Pooling
3D Max Pooling
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Role of Switches
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Overall Architecture (1 layer)
Toy Example
Effect of Pooling
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Talk Overview
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Stacking the Layers
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Overall Architecture (2 layers)
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Multi-layer Inference
Filter Learning
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Overall Algorithm
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Toy Input
Talk Overview
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Related Work
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Comparison: Convolutional Nets
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Related Work
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Talk Overview
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Training Details
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Model Parameters/Statistics
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Model Reconstructions
Layer 1 Filters
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Layer 2 Filters
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Layer 3 filters
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Layer 4 filters
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Relative Size of Receptive Fields
Largest 3 activations at top layer
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Largest 5 activations at top layer
Top-down Decomposition
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Largest 5 activations at top layer
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Application to Object Recognition
FeatureMapsFeatureMaps
Classification Results: Caltech 101
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Classification Results: Caltech 256
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Classification Results:
Transfer Learning
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Classification/Reconstruction
Relationship
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Effect of Sparsity
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0 2 4 6 8 10
Cal
tech
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1 R
eco
gnit
ion
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Number of ISTA iterations in inference
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Analysis of Switch Settings
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Summary
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Model using layer-layer
reconstruction
Single Deconvolutional Layer
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Single Deconvolutional Layer
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Single Deconvolutional Layer
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animal head instantiated by bear head
e.g. discontinuities, gradient
e.g. linelets, curvelets, T-junctions
e.g. contours, intermediate objects
e.g. animals, trees, rocks
Context and Hierarchy in a Probabilistic Image ModelJin & Geman (2006)
A Hierarchical Compositional System for Rapid Object Detection
Long Zhu, Alan L. Yuille, 2007.
Able to learn #parts at each level
Comparison: Convolutional Nets
LeCun et al. 1989
Deconvolutional Networks
• Top-down decomposition with convolutions in feature space.
• Non-trivial unsupervised optimization procedure involving sparsity.
Convolutional Networks
• Bottom-up filtering with convolutions in image space.
• Trained supervised requiring labeled data.
Learning a Compositional Hierarchy of Object
Structure
Fidler & Leonardis, CVPR’07; Fidler, Boben & Leonardis, CVPR 2008
The architecture
Parts model
Learned parts