在SVM时代,margin (以下称作间隔)被认为是模型泛化能力的保证,但在神经网络时代使用的最多的损失函数 Softmax 交叉熵损失中并没有显式地引入间隔项。从第一篇和第三篇文章中我们知道通过 smooth 化,可以在类间引入一定的间隔,而这个间隔与特征幅度和最后一个内机层的权重幅度有关。但这种间隔的大小主要是由网络...
原始的softmax loss函数为: f表示的是最后一个全连接层的输出(fi表示的是第i个样本),Wj表示的是最后全连接层的第j列。WyiTfi被叫做target logit 在A-softmax损失函数中,则是会对权重向量进行归一化,即||Wi|| = 1,并将target logit从 ||fi||cos(θyi) 改成 ||fi||ψ(θyi): m通常是一个比1大...
首先，Large-Margin Softmax (L-Softmax)，源自论文"Large-Margin Softmax Loss for Convolutional Neural Networks"。L-Softmax在原始Softmax的基础上引入了正整数m，增加了分类面的间隔，使得类别间的特征更加分明，而类内特征更加紧凑。接着，SphereFace (A-Softmax)，由"SphereFace: Deep Hypersphere...
ADDITIVEMARGINSOFTMAX 在我们的方法中定义: 其与A-Softmax中定的m的效果类似,可以达到减小对应标签项的概率,增大损失的效果,因此对同一类的聚合更有帮助 对权重和特征都进行归一化,添加一个归一化层在全连接层后面: 所以前向传播只用计算: 然后根据NormFace中的概念使用一个超参数s来scale这个cosine值,最后损失函数...
基于Caffe的Large Margin Softmax Loss的实现(上) 小喵的唠叨话:在写完上一次的博客之后,已经过去了2个月的时间,小喵在此期间,做了大量的实验工作,最终在使用的DeepID2的方法之后,取得了很不错的结果。这次呢,主要讲述一个比较新的论文中的方法,L-Softmax,据说单model在LFW上能达到98.71%的等错误率。更重要...
摘要: In deep classification, the softmax loss (Softmax) is arguably one of the most commonly used components to train deep convolutional neural networks (CNNs). However, such a widely used loss is limited...关键词:CNN Softmax L-Softmax SM-Softmax Classification ...
Additive margin softmaxConvolutional neural networks (CNN), more recently, have greatly increased the performance of face recognition due to its high capability in learning discriminative features. Many of the initial face recognition algorithms reported high performance in the small size Labeled Faces ...
This is the implementation of paper <Additive Margin Softmax for Face Verification> - Joker316701882/Additive-Margin-Softmax
mxnet version of Large-Margin Softmax Loss for Convolutional Neural Networks. Derivatives I put all formula I used to calculate the derivatives below. You can check it by yourself. If there's a mistake, please do tell me or open an issue. The derivatives doesn't include lambda in the pape...
We hence introduce a soft-margin softmax function to explicitly encourage the discrimination between different classes. On the other hand, the learned classifier of softmax loss is weak. We propose to assemble multiple these weak classifiers to a strong one, inspired by the recognition that the ...