ResNet, short for Residual Network, is a type of deep learning architecture that has become a cornerstone in computer vision tasks. Developed by researchers at Microsoft, ResNet introduced the concept of residual learning, which addresses the problem of vanishing gradients as neural networks grow deeper. The key innovation in ResNet is the skip connection, which allows the input of a layer to bypass one or more layers and directly connect to a later layer. This mechanism enables the network to learn residual mappings—essentially, the differences between the input and output—rather than trying to learn the full mapping outright. As a result, deeper networks can converge more easily and avoid performance degradation. ResNet has been widely used for tasks like image classification, object detection, and segmentation. Its architecture has variants such as ResNet-18, ResNet-34, ResNet-50, and ResNet-101, where the numbers represent the depth of the network. ResNet’s efficiency and accuracy have made it a go-to choice for many applications in machine learning and AI.
What is ResNet?
Keep Reading
How is self-supervised learning different from unsupervised learning?
Self-supervised learning and unsupervised learning are two different approaches to training machine learning models, and
What is SHAP (Shapley Additive Explanations)?
SHAP, or Shapley Additive Explanations, is a method used to explain the output of machine learning models. It is based o
What is a data steward, and what do they do?
A data steward is a professional responsible for managing an organization's data assets. Their primary role involves ens