Triplet loss Triplet Loss首次在FaceNet: A Unified Embedding for Face Recognition and Clustering这篇论文中被提出,目的是使网络学到更好的人脸表征,即同一个人的不同输入通过网络输出的 表征间距离尽量小,不同人得到的 表征距离尽量大。 不同于Contrastive Loss,Triplet Loss构造了一个三元组计算损失。<a,p,n...
在构建loss时pytorch常用的包中有最常见的MSE、cross entropy(logsoftmax+NLLLoss)、KL散度Loss、BCE、HingeLoss等等,详见:https://pytorch-cn.readthedocs.io/zh/latest/package_references/torch-nn/#loss-functions 这里主要讲解一种考虑类间距离的Center Loss: 一、简介: center loss来自ECCV2016的一篇论文:A Dis...
The proposed contrastive-center loss simultaneously considers intra-class compactness and inter-class separability, by penalizing the contrastive values between: (1)the distances of training samples to their corresponding class centers, and (2)the sum of the distances of training samples to their non-...
Contrastive center lossEnhanced detail featuresDeep cross-modalIn recent years, 3D model retrieval has become a hot topic. With the development of deep learning technology, many state-of-the-art deep learning based multi-view 3D model retrieval algorithms have emerged. One of the major challenges ...
Furthermore, SACT introduces a new loss function, the contrastive鈥揷enter loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments ...