1. The Role of Learning in Vision 3.30pm: Rob Fergus 3.40pm: Andrew Ng 3.50pm: Kai Yu 4.00pm: Yann LeCun 4.10pm: Alan Yuille 4.20pm: Deva Ramanan 4.30pm: Erik Learned-Miller 4.40pm: Erik Sudderth 4.50pm: Spotlights - Qiang Ji, M-H Yang 4.55pm: Discussion 5.30pm: End Feature / Deep Learning Compositional Models Learning Representations Overview Low-level Representations Learning on the fly
2. An Overview of Hierarchical Feature Learning and Relations to Other Models Rob Fergus Dept. of Computer Science, Courant Institute, New York University
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6. Single Layer Architecture Filter Normalize Pool Input: Image Pixels / Features Output: Features / Classifier Details in the boxes matter (especially in a hierarchy) Links to neuroscience
7. Example Feature Learning Architectures Pixels / Features Filter with Dictionary (patch/tiled/convolutional) Spatial/Feature (Sum or Max) Normalization between feature responses Features + Non-linearity Local Contrast Normalization (Subtractive / Divisive) (Group) Sparsity Max / Softmax
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Editor's Notes
Winder and Brown paper. Slightly smoothed view of things.
Note pooling is across space, not across Gabor channel
Non-maximal suppression across VW. Like an L-Inf normalization
Note pooling is across space, not across Gabor channel
