CNN Fundamental 3- Why Residual Networks ResNet Works

A residual network, also known as ResNet, is a deep learning architecture that revolutionized the field of computer vision. It was introduced in the paper "Deep Residual Learning for Image Recognition" by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun in 2016.
ResNet addresses the challenge of training very deep neural networks by introducing a concept called residual blocks. In traditional deep networks, as the number of layers increases, the vanishing gradient problem can occur, making it difficult for the network to learn effectively. The vanishing gradient problem refers to the diminishing magnitude of gradients during backpropagation, hindering the training process.
ResNet tackles this problem by utilizing skip connections or shortcut connections. These connections allow the network to learn residuals, i.e., the difference between the input and the desired output. The core idea is that instead of trying to learn the direct mapping from input to output, ResNet learns the residual mapping. By adding the original input to the output of each layer (through the skip connection), the network can bypass the vanishing gradient issue.
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[@rajeshadepu977]

Excellent

[@KGPTalkie]

Thanks

[@teetanrobotics5363]

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[@user-sh5hn2gn1k]

Hi, Hope your'e doing well.
How does ResNet address overfitting?
How vanishing gradients problem and overfitting are related with each other?
Kindly elaborate, Thanks!
ResNet addresses vanishing gradient problem.