ResNets

Residual Networks, often abbreviated as ResNets, are a class of deep convolutional neural networks that have significantly advanced the field of computer vision. The primary innovation in ResNets is the introduction of "residual blocks." These blocks allow the network to learn residual functions, which are essentially the differences between the input and the desired output of a layer. This is achieved through the use of "skip connections" or "shortcut connections."

These skip connections bypass one or more layers and add the input of the block directly to the output of the block. This mechanism helps to alleviate the vanishing gradient problem, which commonly plagues very deep neural networks. When gradients are backpropagated through many layers, they can become very small, making it difficult for earlier layers to learn effectively. By providing a direct path for the gradient, skip connections ensure that information can flow more easily through the network, even in very deep architectures.

The ability to train much deeper networks than previously possible without performance degradation has led to substantial improvements in accuracy on various computer vision tasks, including image classification, object detection, and semantic segmentation. ResNets have become a foundational architecture in modern deep learning research and applications.