Deep Residual Learning for Image Recognition

ResNet analysis: how skip connections and residual learning solved the degradation problem, enabling training of 100+ layer neural networks.

Kaiming He, Xiangyu Zhang +215 min read|Original Paper|Computer VisionCNNResNet+1
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Paper Overview

The paper introduces the ResNet architecture, which has become the foundation of modern computer vision. It completely eliminates recurrence and convolutions, relying entirely on attention mechanisms to draw global dependencies between input and output.

Key Contributions

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