Learn Before
Shortcut’s technique for identity mapping
• The "identity shortcuts" are referring to performing the element wise addition of x with the output of the residual layers.
• We consider a building block defined as: y = F (x, {Wi}) + x, where x and y are the input and output vectors of the layers considered, the function F (x, {Wi}) represents the residual mapping to be learned.
• The residual mapping (F (x, {Wi})) becomes y= W2σ2(W1x)+x • To sum up, we take the output x of a layer skip it forward and element wise sum it with the output of the residual mapping and thus produce a residual block.
0
1
Tags
Data Science
Related
Recent Variants of ResNets
Advantages of ResNets
Plain vs. ResNets Convolutional Neural Network Architectures
Evaluate ResNet at different depths for ImageNet Classification
Evaluate ResNet models with other state-of-the-art models for ImageNet Classification
Shortcut’s technique for identity mapping
Deep Residual Learning for Image Recognition
Residual Mapping
ResNet Initial Layers
Highway Networks vs. Residual Networks
Influence of Residual Connections on Subsequent Architectures
Adding Layers During Training in Residual Networks
Accelerated Forward Propagation in Residual Networks