A residual connection is a technique used in deep learning, particularly in neural networks, to help improve their training and performance. The concept was popularized by the ResNet (Residual Network) architecture, which won the ImageNet competition in 2015.
In a typical neural network, data flows sequentially through layers, where each layer applies certain transformations. However, as networks become deeper (with more layers), they can experience issues such as vanishing gradients, where the gradients used to update weights during training become very small, hindering learning.
Residual connections address this problem by allowing the input to a layer to bypass one or more layers and be added directly to the output of those layers. This is mathematically represented as:
Output = F(Input) + Input
Here, F(Input) represents the transformation applied by the layers being bypassed. By including the original input in the output, residual connections help maintain the flow of information and gradients, making it easier for the network to learn complex patterns.
These connections also allow for the training of much deeper networks, leading to better performance on various tasks like image recognition, natural language processing, and more. Overall, residual connections are a crucial innovation in modern deep learning, facilitating the development of more sophisticated AI models.