ResidualNetze

ResidualNetze, also known as Residual Networks or ResNets, are a class of deep convolutional neural networks that have significantly impacted the field of deep learning. Their primary innovation lies in the introduction of "skip connections" or "residual blocks." These connections allow the network to learn residual functions, effectively bypassing one or more layers. This architectural change addresses the vanishing gradient problem, a common issue in training very deep neural networks where gradients become progressively smaller as they propagate backward through the layers, hindering effective learning.

The core idea behind residual blocks is that instead of learning a direct mapping from an input x to an output H(x), the network learns the residual mapping F(x) = H(x) - x. The output of the block is then H(x) = F(x) + x. By adding the identity mapping (the input x) back to the output of the stacked layers (F(x)), the network is encouraged to learn small adjustments or residuals, making it easier for gradients to flow through the network during backpropagation. This design facilitates the training of much deeper networks than previously possible, leading to state-of-the-art performance on various computer vision tasks.

ResidualNetze have been instrumental in advancing image recognition, object detection, and semantic segmentation. Their ability to train extremely deep architectures has been a key factor in the success of many modern deep learning models. The concept of residual learning has also been adapted and applied to other neural network architectures beyond convolutional networks.