Identity Mappings in Deep Residual Networks
This paper analyzes propagation in deep residual networks, showing that identity skip connections and identity after-addition activation enable direct forward and backward signal flow; it then proposes a pre-activation residual unit that eases optimization and improves accuracy, enabling very deep ResNets with strong performance.
Episodes
Chapters
01Abstract
Identity mappings in deep residual networks enable direct signal propagation, facilitating training and improving generalization.
1:47Explained02Introduction
Deep residual networks leverage residual functions with identity skip connections to learn additive residuals, enabling the training of very deep architectures.
1:24Explained03Analysis of Deep Residual Networks
When both skip connections and after-addition activations are identity mappings, signals can propagate directly through the network, easing optimization.
2:00Explained04Discussions
Direct signal propagation is facilitated by identity skip connections and identity after-addition activation, forming clean information paths.
1:49Explained05On the Importance of Identity Skip Connections
Modifying identity skip connections with scaling, gating, or 1x1 convolutions impedes signal propagation and leads to optimization difficulties.
1:42Explained06Experiments on Skip Connections
Experiments show that constant scaling, exclusive gating, shortcut-only gating, 1x1 convolutional shortcuts, and dropout on shortcuts degrade performance compared to identity skip connections.
1:53Explained07On the Usage of Activation Functions
The placement of activation functions, specifically in relation to element-wise addition, significantly impacts training and performance in deep residual networks.
2:08Explained08Experiments on Activation
Pre-activation with Batch Normalization and ReLU, compared to post-activation, eases optimization and improves regularization, leading to better results.
1:56Explained09Analysis
Pre-activation eases optimization for very deep networks and improves regularization, reducing overfitting and enhancing generalization.
1:48Explained10Conclusions
Identity shortcut connections and identity after-addition activation are crucial for smooth information propagation in deep residual networks, enabling training of extremely deep models.
2:00Explained