【深度学习】Loss Functions for Neural Networks for Image Processing

时间 2020-12-27

标签深度学习繁體版

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在目前的深度学习中，业界主流主要还是把调整深度学习网络结构作为主要的工作重心，即使损失函数(loss functions)对整个网络的训练起着十分重要的作用。 Nvidia和MIT最近发了一篇论文《loss functions for neural networks for image processing》则详细探讨了损失函数在深度学习起着的一些作用。通过对比L1,L2,SSIM,MS-SSIM

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1. 阅读论文《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》笔记
6. Weakly-supervised convolutional neural networks for multimodal image registration
7. 读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
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1. 阅读论文《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》笔记
6. Weakly-supervised convolutional neural networks for multimodal image registration
7. 读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

>>更多相关文章<<