Residual Network

What is Residual Network (ResNet)? ResNet Explained

Residual Network (ResNet) is a deep learning architecture that was introduced by Kaiming He et al. in 2015 to address the problem of vanishing gradients in very deep neural networks. ResNet achieved remarkable success and became a cornerstone in computer vision tasks, such as image classification and object detection.

The key innovation of ResNet lies in the introduction of residual connections, also known as skip connections or shortcut connections, that allow the network to bypass one or more layers. These connections enable the network to learn residual mappings, focusing on the differences between the input and the desired output, rather than trying to directly learn the entire mapping.

Here are some key characteristics and components of ResNet:

Residual Blocks: The basic building block of ResNet is the residual block. It consists of multiple convolutional layers with a shortcut connection that directly connects the input of the block to its output. The shortcut connection bypasses the convolutional layers, allowing the network to learn the residual mapping. Residual blocks can be stacked together to create deep networks with hundreds of layers.

Identity Mapping: In ResNet, the shortcut connection is often implemented as an identity mapping, where the input is directly added to the output. This helps ensure that the information from the input can flow easily through the network, preventing the vanishing gradient problem and allowing for better gradient flow during training.

Skip Connections: The skip connections in ResNet allow for the preservation of information from earlier layers, making it easier for the network to learn and propagate gradients. These connections help in alleviating the degradation problem, where adding more layers leads to a degradation in accuracy due to diminishing training error.

Residual Learning: The concept of residual learning is central to ResNet. Instead of directly learning the mapping function from the input to the output, ResNet focuses on learning the residual mapping, which captures the difference between the input and the output. This residual learning approach allows for the easier learning of identity mappings and facilitates the training of deep networks.

Architecture Variants: ResNet has different variants based on the number of layers, such as ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. These variants have different depths and layer configurations, providing flexibility to choose the suitable architecture based on the complexity of the task and available computational resources.

ResNet has demonstrated superior performance compared to previous deep learning architectures, especially when dealing with very deep networks. It has achieved state-of-the-art results in various computer vision tasks, including image classification, object detection, and image segmentation. The residual connections in ResNet enable the effective training of deep networks and have become a widely adopted technique in the field of deep learning.