Deep Residual Learning for Image Recognition

This paper introduces a novel deep learning framework called Residual Networks (ResNets) to address the problem of training very deep neural networks. ResNets make it possible to train networks with hundreds or even thousands of layers by introducing residual connections, which allow the network to learn residual functions with reference to the layer inputs, instead of trying to learn the underlying mapping directly. This approach significantly eases the training process and enables the creation of much deeper and more accurate models, achieving state-of-the-art results in image classification tasks.