Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
这篇文章提出的问题是感知损失中由于一开始vgg训练的目的与超分目的不符，导致用感知损失约束的网络会产生不符合原本分布的HR伪影。 vgg一开始训练的目的是为了进行分类任务这导致在前面30层中利用conv进行特征提取时对特征很敏感，即输入的特征图的某部分特征会为了去归为某一类而导致产生的特征图有不符合原分布的特征。 这篇文章提出的解决办法为利用对抗网络中间层生成的特征作损失约束网络去除伪影，对抗网络
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相关文章
- 1. IMPROVING GENERATIVE ADVERSARIAL NETWORKS WITH DENOISING FEATURE MATCHING（Bingio-ICLR2017）
- 2. perceptual loss 和adversial loss
- 3. Adversarial learning with multi-scale loss for skin lesion segmentation
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- 5. 2D Features framework7(Feature Matching with FLANN )
- 6. ExFuse- Enhancing Feature Fusion for Semantic Segmentation笔记
- 7. Enhancing Underwater Imagery using Generative Adversarial Networks
- 8. 【文献阅读】Perceptual Generative Adversarial Networks for Small Object Detection –CVPR-2017
- 9. Finding Tiny Faces in the Wild with Generative Adversarial Network
- 10. [论文笔记]FashionBERT: Text and Image Matching with Adaptive Loss for Cross-modal Retrieval