不幸的是,大家普遍的认识是the triplet loss 不如使用surrogate losses (classification, verification) followed by a separate metric learning step. 我们表明,对于从头开始训练的模型 或者是 预训练的模型,使用the triplet loss 的一种变体 来执行端到端的深度度量学习,比大多数其他已发表的方法有很大的优势。 引言...
Retinal artery/vein (A/V) classification lays the foundation for the quantitative analysis of retinal vessels, which is associated with potential risks of various cardiovascular and cerebral diseases. The topological connection relationship, which has been proved effective in improving the A/V ...
triplet loss 的最大特点是，它不会去过分要求同样标签的样本在向量空间中的距离要相近。
Triplet loss通常能比classification得到更好的feature。还有一个优点就是Triplet loss可以卡阈值,Triplet loss训练的时候需要设置一个margin,这个margin可以控制正负样本的距离,当feature 进行normalization后,可以更加方便的卡个阈值来判断是不是同一个ID。当然Triplet loss也有缺点,就是收敛慢,而且比classification更容overfit...
代码已做过gpu上训练的适配: triplet_loss.py 实现策略: 我们选择的是batch_hard策略,即在一个batch中,循环定义每一个样本为anchor,选择距离anchor最大的positive exmaple 记为:d(a,p);选择距离anchor最小negtive example,记为d(a,n),我们的目标是通过训练尽可能缩小d(a,p),增大d(a,n);对于1个anchor来...
Classification Loss: 当目标很大时,会严重增加网络参数,而训练结束后很多参数都会被摒弃。 Verification Loss: 只能成对的判断两张图片的相似度,因此很难应用到目标聚类和检索上去。因为一对一对比太慢。 但是Triplet Loss还是很吸引人啊: 端到端,简单直接; 自带聚类属性; 特征高度嵌入。
Towards that aim, this paper presents a Siamese Convolutional Neural Network (CNN) based model using the Triplet-loss function for the 4-way classification of AD. We evaluated our models using both pre-trained and non-pre-trained CNNs. The models' efficacy was tested on the OASIS dataset ...
{zhichaozhou,songbai,xbai}@hust.edu.cn Abstract Most existing 3D object recognition algorithms focus on leveraging the strong discriminative power of deep learn- ing models with softmax loss for the classification of 3D data, while learning discriminative features with deep met- ric learning for ...
1 原理 triplet loss的计算是基于一个三元组计算同类样本和不同类样本之间的距离差，如式子(1)所示，...
标题取得是真好,这篇文章要做的就是要捍卫Triplet loss在Person Re-Identification中的表现,可以说是为Triplet loss正名,告诉大家——Triplet loss不比classification loss和verification loss差。这篇文章其实可以浓缩为一句话:A well designed triplet loss has a significant impact on the result.Triplet loss是metric...