【深度學習】Loss Functions for Neural Networks for Image Processing
在目前的深度學習中，業界主流主要還是把調整深度學習網絡結構作爲主要的工作重心，即使損失函數(loss functions)對整個網絡的訓練起着十分重要的作用。 Nvidia和MIT最近發了一篇論文《loss functions for neural networks for image processing》則詳細探討了損失函數在深度學習起着的一些作用。通過對比L1,L2,SSIM,MS-SSIM
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相關文章
- 1. 閱讀論文《Loss Functions for Image Restoration With Neural Networks》
- 2. 【One Shot】《Siamese Neural Networks for One-shot Image Recognition》
- 3. Bag of Tricks for Image Classification with Convolutional Neural Networks
- 4. CS231n Convolutional Neural Networks for Visual Recognition
- 5. 《Siamese Neural Networks for One-shot Image Recognition》筆記
- 6. Weakly-supervised convolutional neural networks for multimodal image registration
- 7. 讀Exploring Randomly Wired Neural Networks for Image Recognition
- 8. Bag of Tricks for Convolutional Neural Networks
- 9. RandWireNN: Exploring Randomly Wired Neural Networks for Image Recognition
- 10. Large-Margin Softmax Loss for Convolutional Neural Networks