residuallearning

Residual learning is a concept introduced in the field of deep learning, particularly in the context of convolutional neural networks (CNNs). It was first proposed by Kaiming He and his colleagues in their 2015 paper titled "Deep Residual Learning for Image Recognition." The core idea behind residual learning is to ease the training of very deep neural networks by introducing shortcut connections, also known as skip connections, which allow the network to learn residual functions rather than direct mappings.

In traditional deep neural networks, each layer learns to transform its input to produce an output. However, as the network depth increases, the gradients can vanish or explode, making it difficult to train the network effectively. Residual learning addresses this issue by allowing the network to learn the residual, or difference, between the input and the desired output. This is achieved by adding the input of a layer to its output, effectively creating a shortcut connection that bypasses one or more layers.

The residual learning framework can be represented mathematically as follows: let H(x) be the desired underlying mapping to be fit by a few stacked layers, and let F(x) be the mapping to be fit by the stacked nonlinear layers. Instead of learning H(x), the network learns F(x) = H(x) - x, where x is the input to the layers. The original mapping H(x) can then be obtained by adding the input x to the output of the layers: H(x) = F(x) + x.

Residual learning has been shown to significantly improve the training of very deep networks, enabling the training of networks with hundreds or even thousands of layers. This has led to significant advancements in various computer vision tasks, such as image classification, object detection, and segmentation. The concept of residual learning has also been extended to other types of neural networks and has inspired further research in the field of deep learning.