WebAnd Circlesoftmax [4], through the introduction of the classified super plane during the training, make different types of features of different categories when testing make it easier to distinguish. As shown below, it is a training test process of Cosface [8] and CirclesoftMax [4], respectively. Web[Verse 2] Here we are again, in the rain, oh 'Bout to throw your ring on the tracks (On the tracks) Right under the very same train Where I told you I loved you So don't tell me it's …
对比学习损失篇,从L-softmax到AM-softmax到circle-loss到PCCL
文中提出了一种基于对的相似性优化方法,基于对的优化的方法目的都是最大化类内相似性 s_p 同时最小化类间相似性 s_n 。文中发现大多数的损失函数,包括triplet loss 和softmax激活函数加交叉熵损失函数,都是使 s_n 和 s_p 嵌入到一个相似性对,并且去减小 (s_n-s_p) 。这样的优化方式是不够灵活的,因为其对每一个单 … See more Circle loss的思想还是根据相似得分来对其反向传播的权重进行动态调整,这点是和focal loss[7]是一样的,focal loss是根据分类的概率动态调整反向传播的权重的。文中提到的Multi-Similarity … See more WebMar 29, 2024 · Circle Loss 非常简单,而它对深度特征学习的意义却非常本质,表现为以下三个方面: 统一的(广义)损失函数。 从统一的相似度配对优化角度出发,它为两种基本学习范式(即使用类别标签和使用样本对标签的学习)提出了一种统一的损失函数; 灵活的优化方式。 在训练期间,向 s_n 或 s_p 的梯度反向传播会根据权重 α_n 或 α_p 来调整幅 … the punisher origin story
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