Deep Learning: Chapter 9
9 Convolutional Networks
9.1 The Convolution Operation
9.2 Motivation
Three important ideas:
- sparse interactions
- parameter sharing
- equivariant representations
9.3 Pooling
9.4 Convolution and Pooling as an Infinitely Strong Prior
One key insight is that …
Another key insight is that …
9.5 Variants of the Basic Convolution Function
stride
padding
- valid
- same
- full
locally connected layer(unshared convolution)
Tiled convolution
There three operations - convolution, backprop from output to weight, and backprop from output to inputs - are sufficient to compute all the gradients needed to train any depth of feedforward convolutional network.
9.6 Structured Outputs
9.7 Data Types
9.8 Efficient Convolution Algorithms
9.9 Random or Unsupervised Features
There are three basic strategies for obtaining convolution kernels without supervised training.
- initialize them randomly
- unsupervised learning approach
- intermediate approach