Residual Learning

Residual learning is a deep learning technique that improves the training of deep neural networks by adding "skip connections" which allow the network to learn residual functions, effectively bypassing the vanishing gradient problem and enabling the training of significantly deeper architectures. Current research focuses on integrating residual learning into various model architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, for applications ranging from image processing and computer vision to natural language processing and time series forecasting. The widespread adoption of residual learning has significantly advanced the performance of numerous deep learning models across diverse fields, leading to improvements in accuracy, efficiency, and generalization capabilities.