ResNet (Residual Network) is a groundbreaking deep learning architecture introduced by Kaiming He et al. in 2015. It addresses the vanishing gradient problem in deep networks through residual blocks and skip connections, enabling the training of extremely deep models (e.g., 152 layers) with remarkable accuracy.

Key Features

  • 📌 Deep Networks: ResNet can have up to 152 layers, surpassing traditional limitations.
  • 🔄 Skip Connections: Residual blocks allow shortcuts for information flow, mitigating gradient issues.
  • 🧠 Identity Mapping: Uses a linear transformation to learn identity functions, improving training efficiency.
  • 📈 State-of-the-Art Performance: Achieved record-breaking results in tasks like ImageNet classification.

Applications

  • 📊 Image Recognition: Widely used in CNNs for tasks like object detection and classification.
  • 🤖 Computer Vision: Foundations for models like Faster R-CNN and YOLO.
  • 🌐 Research & Education: A popular topic for tutorials and academic papers.

For a deeper dive into ResNet implementation, check out our ResNet_Tutorial guide.

ResNet_Architecture

Explore related concepts like Deep_Learning_Models or Neural_Networks to expand your knowledge!