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Y LeCun
Plan
The motivation for ConvNets and Deep Learning: end-to-end learning
Integrating feature extractor, classifier, contextual post-processor
A bit of archeology: ideas that have been around for a while
Kernels with stride, non-shared local connections, metric learning...
“fully convolutional” training
What's missing from deep learning?
1. Theory
2. Reasoning, structured prediction
3. Memory, short-term/working/episodic memory
4. Unsupervised learning that actually works
Post-
Processor
Low-Level
Features
More
Features
Classifier
Y LeCun
Deep Learning = Learning Hierarchical Representations
Traditional Pattern Recognition: Fixed/Handcrafted Feature Extractor
Trainable
Classifier
Feature
Extractor
Mainstream Modern Pattern Recognition: Unsupervised mid-level features
Trainable
Classifier
Feature
Extractor
Mid-Level
Features
Deep Learning: Representations are hierarchical and trained
Trainable
Classifier
Low-Level
Features
Mid-Level
Features
High-Level
Features
Y LeCun
Early Hierarchical Feature Models for Vision
[Hubel & Wiesel 1962]:
simple cells detect local features
complex cells “pool” the outputs of simple
cells within a retinotopic neighborhood.
Cognitron & Neocognitron [Fukushima 1974-1982]
pooling
subsampling
“Simple cells”
“Complex
cells”
Multiple
convolutions
Y LeCun
The Mammalian Visual Cortex is Hierarchical
[picture from Simon Thorpe]
[Gallant & Van Essen]
The ventral (recognition) pathway in the visual cortex has multiple stages
Retina - LGN - V1 - V2 - V4 - PIT - AIT ....
Lots of intermediate representations
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