https://blog.csdn.net/qq_16234613/article/details/81210320 Angular Loss: Deep Metric Learning with Angular Loss 从角度相似性方向考虑对triplet loss,增强了其尺度不变性,并且考虑了3阶几何约束。这篇文章的逻辑推理很nice。 Ranked List Loss for Deep Metric Learning CVPR2019 度量学习 Ranked List Loss ...
CVPR2019 度量学习 Ranked List Loss for Deep Metric Learning 原文链接:https://arxiv.org/abs/1903.03238 度量学习在图像识别、检索等领域有着广泛应用。本文提出了一种新的方法,Ranked List Loss(RLL)。利用该损失函数可以小幅提升网络的准确性。话不多说,直接上图。 上图是几种常见的损失函数,最右侧的是本...
深度度量学习(Deep Metric Learning, DML) 在很多场景中都发挥着重要的作用,一个经典的应用就是人脸识别,Google 的FaceNet模型使用Triplet-Loss刷新了当时人脸识别的记录,近两年基于Softmax改进的Angular-Softmax, Additive Margin Softmax等在人脸识别成功应用的损失函数,都引入了度量学习的思想。 基于深度学习的度量学习...
深度度量学习(Deep Metric Learning, DML) 在很多场景中都发挥着重要的作用,一个经典的应用就是人脸识别,Google 的FaceNet模型使用Triplet-Loss刷新了当时人脸识别的记录,近两年基于Softmax改进的Angular-Softmax, Additive Margin Softmax等在人脸识别成功应用的损失函数,都引入了度量学习的思想。 基于深度学习的度量学习...
作者先是回顾了目前存在的一些loss, 从上图可以看到,ranked list loss也就是本文提出的方法,在训练中充分利用了输入样本信息。 本文的想法是把正例样本与负例样本以mm隔开,类内样本允许存在α−mα−m的分布差异,如下图所示: 成对约束: 其基于成对损失,上图可以表示为:Lm(xi,xj;f)=(1−yij)[α−...
Loss function "ranked_loss", SoftMax(LS) + w*RLL, results as "Results". For RLL I useRanked List Loss for Deep Metric Learning, and made some modifications. "cranked_loss", SoftMax(LS) + w*RLL(kmeans), before compute RLL i use kemeans to cluster features to help find hard samples...
Clustering is a well-known unsupervised machine learning approach capable of automatically grouping discrete sets of instances with similar characteristics
and introduces the KL divergence loss of the data distribution metric, so that within the Batch The two data distributions generated by the same sample should be as close as possible and have distribution consistency. Specifically, for each training sample, R-Drop minimizes the KL divergence betwee...
Jenkins used to be the default for many projects as it has the biggest community along with a very powerful platform at the price of a complex setup that demands a steep learning curve. Nowadays, it has become much easier to set up a CI solution using SaaS tools like CircleCI and others...
In the era of big data when a huge amount of data is continuously being generated, it is common for situations to arise where the number of samples is much smaller than the number of features (variables) per sample. This phenomenon is often found in biom