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Unsupervised Feature Learning by Deep Sparse Coding
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Yunlong He, Georgia TechKoray Kavukcuoglu, DeepMind TechnologiesYun Wang, Princeton UniversityArthur Szlam, The City College of New YorkYanjun Qi, University of Virginia
Two lines of Previous Research• Bag of Visual words
• Deep learning
• Motivation: Can we combine the power of two methods?
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Image Dense CodePyramid Pooling ClassifierSparse Code
Input Image Features
Encoder
DecoderFeatures
Encoder
Decoder
Class label
Encoder
Decoder…
Repeat[Hinton & Salakhutdinov 06, 09]
Layer 1 Layer n [LeCun et al. 98]
[Lazebnik et al. 06][Yang et al. 09]
SIFT Descriptors
Background: Bag of Visual words
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse Code
Background: Bag of Visual words
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Image
SIFT Descriptors
Image
Dense Code
Background: Bag of Visual words
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Example:900 Patches
Image
Dense Code
Background: Bag of Visual words
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SIFT filter
SIFT Descriptorsfor every patch
Image
Dense Code
Background: Bag of Visual words
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Dense Code
Sparse Code
Background: Bag of Visual words
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Pyramid Pooling
Sparse Code
Level 1 Level 0
21 pooling regions
Concatenate Sparse Codes of 21 pooling regions
Background: Bag of Visual Words
Relatively shallow
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse CodeCan we learn multiple layers of sparse representations?
Deep Sparse Coding
Relatively shallow
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse Code
Image
Dense Code
Pyramid PoolingSparse Code
Dense Code
Sparse Code
Dense Code
Sparse Code
Pyramid Pooling
Pyramid Pooling
Classifier
Unsupervised Deep Architecture!
Deep Sparse Coding
Relatively shallow
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse Code
Image
Dense Code
Pyramid PoolingSparse Code
Dense Code
Sparse Code
Dense Code
Sparse Code
Pyramid Pooling
Pyramid Pooling
Classifier
Unsupervised Deep Architecture!
Deep Sparse Coding
Relatively shallow
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse Code
Image
Dense Code
Pyramid PoolingSparse Code
Dense Code
Sparse Code
Dense Code
Sparse Code
Pyramid Pooling
Pyramid Pooling
Classifier
Unsupervised Deep Architecture!
Dimension expansion
Idea:• Separation (for classification)• Representation (bag of visual words)
Method:• Sparse coding
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Dense Code
Sparse Code
Dimension reduction
Idea:• Invariance• Compositionality
Method:• Locally spatial pooling + Low dimensional embedding• Both unsupervised
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Sparse Code
Dense Code
Locally spatial pooling
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Covers larger area
Low dimensional embedding
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embedding
Unstable Stable
Low dimensional embedding
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embedding
Unstable Stable
Idea: embedding with the help of spatial information
Low dimensional embedding
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embedding
Dimensionality Reduction by Learning an Invariant Mapping (DR. LIM)
Low dimensional embedding
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embedding
Dimensionality Reduction by Learning an Invariant Mapping (DR. LIM)
Deep Sparse Coding
Relatively shallow
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Image
Dense Code
Pyramid Pooling
Classifier
Sparse Code
Image
Dense Code
Pyramid PoolingSparse Code
Dense Code
Sparse Code
Dense Code
Sparse Code
Pyramid Pooling
Pyramid Pooling
Classifier
Unsupervised Deep Architecture!
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Conclusion
§Combine sparse coding with deep learning§Combine dimension expansion and dimension reduction§A connecting function is learned by embedding method§ Combining multiple layers of sparse code achieves state-of-the-art performance on image classification tasks
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