The triplet-center loss can learn more discriminative features than traditional classification loss, and it has been successfully used in deep metric learning-based 3D shape retrieval task. However, it has a hard margin parameter that only leverages part of the training data in each mini-batch. ...
loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=self._margin)else: loss = F.soft_margin_loss(dist_an - dist_ap, y)ifloss == float('Inf'): loss = F.margin_ranking_loss(dist_an, dist_ap, y, margin=0.3)return{"loss_triplet": loss * self._scale, } 开发者ID:JDAI...
self.ranking_loss = nn.SoftMarginLoss() 开发者ID:hwang1996,项目名称:ACME,代码行数:9,代码来源:triplet_loss.py 示例5: __init__ ▲点赞 5▼ # 需要导入模块: from torch import nn [as 别名]# 或者: from torch.nn importSoftMarginLoss[as 别名]def__init__(self):super(TripletLoss_WRT, sel...
更甚,softmax loss不会特定优化人脸验证的需求(即,相同ID的人脸更靠近,不同ID的人脸更远离)。正是基于这个原因,许多方法在softmax特征部分采用度量学习《Unconstrained face verification using deep cnn features》,《Deep face recognition》,《Triplet probabilistic embedding for face verification and clustering》或者...
Center loss Triplet loss Contrastive loss Circle loss相关文献: [1] Large-Margin Softmax Loss for Convolutional Neural Networks [2] SphereFace: Deep Hypersphere Embedding for Face Recognition [3] L2-constrained Softmax Loss for Discriminative Face Verification ...
支持向量机: 上世纪流行的一种用来解决二分类问题的算法。我们使用一组样本{ (xixi,yiyi) }(其中y∈{-1,1},称为标签;xixi为一维向量,称为特征)来构建模型(训练模型)。咋说呢,借助坐标系把这些点的特征在空间中表示出来,使用两种颜色来表示y。需要寻找一个平面把这些点划分为两类。这个平面就是我们的分类器...
参考hinge loss,引入间隔项: L=max(maxi≠y{zi}−zy+m,0), 这个损失函数的意义是: 输出C个分数,使目标分数比最大的非目标分数还要大 m。 这里的m由我们手动进行调节,m 越大,则我们会强行要求目标与非目标分数之间拉开更大的差距。但注意到,如果我们不限制分数z的取值范围,那网络会自动优化使得分数z整体...
) 1.Large-marginSoftmax(L-Softmax) 论文:Large-MarginSoftmaxLossforConvolutional Neural Networks 定义...用于人脸识别的损失函数,有Softmax,ContrastiveLoss,TripletLoss,CenterLoss,Norm Face,Large-MarginLoss 【技术综述】一文道尽softmax loss及其变种 ...
Triplet loss is widely used to push away a negative answer from a certain question in a feature space and leads to a better understanding of the relationship between questions and answers. However, triplet loss is inefficient because it requires two steps: triplet generation and negative sampling....
Triplet loss is widely used to push away a negative answer from a certain question in a feature space and leads to a better understanding of the relationship between questions and answers. However, triplet loss is inefficient because it requires two steps: triplet generation and negative sampling....