Soft Margin Loss在数学上可以表示为: : CosineFace Loss [6] (Large Margin Cosine Loss,CVPR 2018): s\left(\cos \theta_{1}-m-\cos \theta_{2}\right)=0 \tag 4 公式含义和前面一样,只不过在角度上加margin变成了减小余弦值。决策...
In this paper, we propose two multi-view classifiers: multi-view support vector machine via L 0 / 1 soft-margin loss (Mv L 0 / 1 -SVM) and structural Mv L 0 / 1 -SVM (Mv S L 0 / 1 -SVM). The key difference between them is that Mv S L 0 / 1 -SVM additionally fuses ...
MV-Softmax Above softmax loss functions adopt a constant margin for all training datas. However, they ignore the importance of informative features (hard features around boundary line). Hence, MV-Softmax loss emphasises the importance of hard examples, making models focus on the truly informative ...
图4-2 的公式有个良好的性质,它将某个分类错误点的错误程度也放到 loss function 中(也可能是缺点,某些离群点可能会让 SVM 性能下降。为了矫正这个问题,在第 6 节提出了 Tube Regression 可以在一定程度上缓解这个问题)。可用通过 tuning C 来 trade off large margin & noise tolerance !
博客原文:http://www.miaoerduo.com/deep-learning/基于caffe的large-margin-softmax-loss的实现(中).html 四、前馈 还记得上一篇博客,小喵给出的三个公式吗?不记得也没关系。 这次,我们要一点一点的通过代码来实现这些公式。小喵主要是GPU上实现前后馈的代码,因为这个层只是用来训练,GPU速度应该会快一点。
loss.backward() Experiments/Demo There are a simple set of experiments onFashion-MNIST[2] included intrain_fMNIST.pywhich compares the use of ordinary Softmax and Additive Margin Softmax loss functions by projecting embedding features onto a 3D sphere. ...
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 ...
On the one hand, the CNN features learned using the softmax loss are often inadequately discriminative. 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 ...
X2-Softmax loss has adaptive angular margins, which provide the margin that increases with the angle between different classes growing. The angular adaptive margin ensures model flexibility and effectively improves the effect of face recognition. We have trained the neural network with X2-Softmax ...