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What is Understanding Residual Learning in ResNet about?
Residual learning was developed to address issues that arise when training very deep convolutional neural networks. Specifically, accuracy degradation and difficulty of training occur as networks become deeper. Residual networks address these problems by introducing shortcut connections that learn residual functions, allowing signals to propagate directly through identity mappings. This enables training of much deeper networks than was previously possible, with ResNet-152 achieving a top-5 accuracy of 95.51
- Author
- Nhân Hồ
- Language
- EN