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
ResidualNetze have been instrumental in advancing image recognition, object detection, and semantic segmentation. Their ability to