为此作者提出了三个相互配合的模块:渐进生成器“ progressive generator”,像素级鉴别器“ pixel-wise dense detector”以及融合块“ merge block”。简单来说,渐进生成器的作用是在低分辨率修复损坏图像,并逐渐增大其分辨率,在增大分辨率的过程中会逐渐生成细节;像素级鉴别器对于生成的不同分辨率的图像给出对应分辨率的...
论文阅读-Hierarchical Cross-Modal Talking Face Generation with Dynamic Pixel-Wise Loss 论文链接: http://openaccess.thecvf.com/content_CVPR_2019/html/Chen_Hierarchical_Cross-Modal_Talking_Face_Generation_With_Dynamic_Pixel-Wise_Loss_CVPR_2019_paper.html 概述 关键词: 高级空间, 像素抖动, GAN模型 ...
When combined with the standard pixel-wise loss, both our new loss term and our iterative refinement boost the quality of the predicted delineations, in some cases almost doubling the accuracy as compared to the same classifier trained with the binary cross-entropy alone. We show that our ...
Pixel-Wise Contrastive Loss 基于上述分析,文章提出了pixel-wise contrastive distillation(PCD),让student在像素级别上向老师学习。蒸馏的loss如下所示: 其中\ell表示contrastive loss,下文会有更详细的形式。s_i和t_i分别表示student和teacher的feature maps的第i个像素(文章假设了student和teacher的输出feature maps具有...
51CTO博客已为您找到关于pixel-wise loss的相关内容,包含IT学习相关文档代码介绍、相关教程视频课程,以及pixel-wise loss问答内容。更多pixel-wise loss相关解答可以来51CTO博客参与分享和学习,帮助广大IT技术人实现成长和进步。
Learning Multiple Pixelwise Tasks Based on Loss Scale Balancing Jae-Han Lee Korea University jaehanlee@mcl.korea.ac.kr Chul Lee Dongguk University chullee@dongguk.edu Chang-Su Kim Korea University changsukim@korea.ac.kr Abstract We propose a novel loss weighting alg...
The key element of our method is to replace the commonly used binary adversarial loss with a high resolution pixel-wise loss. In addition, we train our generator employing stochastic weight averaging fashion, which further enhances the predicted output label maps leading to state-of-the-art ...
Release code for the paper "Robust pixel-wise illuminant estimation algorithm for images with a low bit-depth"(2024 Optics Express) - shuwei666/Robust-pixel-wise-illuminant-estimation
Inspired by metric learning, we construct the pixel-level pairwise samples and propose a new self-supervised contrastive loss based on them, which makes full use of the class activation maps to reduce the intra-class difference and increase the inter-class difference; we also propose a novel ...
Pixel2Pixel的闪光之处在于:在pair-wised图像编辑任务中,提供了一种额外的优化方法和优化思路,对于仅仅依赖于L1/L2 loss的任务,能够用对抗的思想进一步优化生成网络。文章在论文中进行了实验对照:仅使用L1/L2 loss,仅使用GAN loss,以及结合两者loss对于编辑任务的效果对比。在Cityscape数据集上,使用L1 loss+GAN loss...