ResNet
(Residual Network) is a deep neural network architecture that uses residual connections to help train very deep networks effectively. It was introduced to address difficulties that can arise when increasing network depth.
What is ResNet?
ResNet uses skip connections, which allow information and gradients to pass directly across one or more layers. Instead of learning a complete transformation, a residual block learns the difference, or residual, between the input and desired output. Common variants include ResNet-18, ResNet-50, and ResNet-152.
Why is ResNet Important?
Residual connections help reduce training difficulties associated with very deep neural networks and allow models to learn complex visual patterns more effectively. ResNet has also influenced many later neural network architectures.
Common use cases
ResNet is commonly used for image classification, object detection, image recognition, feature extraction, and other computer vision applications.