Residual connections are a key architectural feature that significantly improve deep learning models, particularly in deep neural networks. They address the problem of vanishing gradients, which can occur as the model gets deeper. In simpler terms, as the number of layers in a neural network increases, the gradients used during training can become very small. This makes it difficult for the model to learn effectively. Residual connections help combat this issue by allowing gradients to flow through the network more easily, making it easier to train deeper models without performance degradation.
The main idea behind residual connections is to skip one or more layers in a network during the forward pass and the backward pass. Instead of learning the output directly, the network learns the difference between the input and the output, or the "residual." This can be expressed mathematically as ( H(x) = F(x) + x ), where ( H(x) ) is the desired output, ( F(x) ) is the transformation performed by the layers, and ( x ) is the input. By focusing on learning the residual, the network can more easily adjust the weights to improve performance, facilitating better learning in deeper architectures.
For example, in ResNet (Residual Networks), which is widely used in image classification tasks, residual connections enable the construction of very deep architectures, such as those with hundreds of layers. These networks have shown remarkable success in benchmarks because they effectively manage to maintain performance as depth increases. By allowing gradients to flow through the network without diminishing, residual connections lead to faster convergence during training and better generalization on unseen data, ultimately resulting in models that are both efficient and powerful.