ImageNet Evolution論文筆記（4）
Deep residual learning for image recognition degradation problem：with the network depth increasing, accuracy gets saturated (which might be unsurprising) and then degrades rapidly。【解決了增加深度帶來的副作用（退化問題）
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相關文章
- 1. 論文筆記4-3
- 2. automl-演化算法-Regularized evolution for image classifier architecture search-論文筆記
- 3. 論文筆記：SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
- 4. 論文筆記SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
- 5. AlexNet論文筆記
- 6. ResNeXt 論文筆記
- 7. 【論文筆記】RCNN
- 8. Xception 論文筆記
- 9. 論文筆記：ImageNet Classification with Deep Convolutional Neural Networks
- 10. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks 論文筆記