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?
- Information Retrieval 101
- Exploring Vector Database Use Cases
- Getting Started with Milvus
- Vector Database 101: Everything You Need to Know
- Evaluating Your RAG Applications: Methods and Metrics
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
What strategies can an application use to hide or tolerate latency in vector retrieval (for example, asynchronous queries, prefetching likely results, or using smaller indexes for quick preliminary filtering)?
Applications can reduce the impact of latency in vector retrieval by decoupling operations from user-facing workflows. A
What is the matrix factorization-based recommender system?
A matrix factorization-based recommender system is a method used to predict a user's preferences by decomposing a large
Where do I get a data set for Hindi characters recognition?
Datasets for Hindi character recognition can be found on platforms like Kaggle, Google Dataset Search, and UCI Machine L