Residual Networks

ResNet, short for Residual Network, is a groundbreaking deep learning architecture introduced by Kaiming He and his colleagues in 2015. It was designed to address the vanishing gradient problem commonly encountered in training very deep neural networks. The learning objective of ResNet is to efficiently train deeper networks and achieve higher accuracy in image recognition and classification tasks.

ResNet’s innovative approach involves the introduction of skip connections or shortcut connections, which allow the network to bypass one or more layers during the forward propagation. These connections enable the model to learn residual mappings, which are the differences between the input and output of a certain layer. This technique not only alleviates the vanishing gradient issue but also enables the training of deeper networks with more layers without sacrificing performance.

ResNet has demonstrated significant improvements in various benchmark datasets like ImageNet and CIFAR, setting new standards in the field of computer vision. Its impact has been substantial, with ResNet-inspired architectures becoming a foundation for many subsequent developments in deep learning and computer vision.