Hinge Loss是一种目标函数(或者说损失函数)的名称,有的时候又叫做max-margin objective。其最著名的应用是作为SVM的目标函数。 其二分类情况下,公式如下: l(y)=max(0,1−t⋅y) 其中,y是预测值(-1到1之间),t为目标值(±1)。 其含义为,y的值在-1到1之间就可以了,并不鼓励|y|>1,即并不鼓励分类...
这里我的理解是,分子的类内距离尽可能小,分母的类间距离尽可能大,这样算出来的margin loss就会尽可能小,得到的margin loss的极大似然估计就是max-margin的估计。 训练损失为\arg \min _{\theta} \mathcal{L}_{t r n}=\mathcal{L}_{d e t}+\lambda \mathcal{L}_{m r g} c. Finetuning: Margin...
Am-Softmax Loss [5] (Additive margin softmax loss,IEEE SPL 2018): 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...
基于Caffe的Large Margin Softmax Loss的实现(中) 小喵的唠叨话:前一篇博客,我们做完了L-Softmax的准备工作。而这一章,我们开始进行前馈的研究。 小喵博客:http://miaoerduo.com 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...
To be specific, we realize the function by L2 normalizing both features and weight vectors in the softmax loss, together with a cosine margin term to maximize the decision margin in the angular space. In addition, max margin constraint, as one regularization term, is incorporated into the ...
Large-Margin Softmax Loss for Convolutional Neural Networks 在本文中,我们将软最大损耗定义为交叉熵损耗、软最大函数和最后一个完全连接层的组合(见图1)。在这种定义下,许多流行的CNN模型可以被视为卷积特征学习组件和softmax损失组件的组合,如图1所示。
Then we can replace the margin loss with the expected margin loss for the classification. So we introduce $$\begin{aligned} \varphi ({\mathbf {y}}|{\mathbf {Z}},\varvec{\eta }) =\prod _{n=1}^{N}\prod _{c=1}^{Nc}\exp \{-2C\cdot \max (0,1 - y^{(n)}_c \varvec...
人脸识别:Large-Margin Softmax Loss )。Large-Marginsoftmaxloss来自ICML2016的论文:Large-MarginSoftmaxLossforConvolutionalNeuralNetworks论文链接...学习到的参数可以将两类样本的类间距离加大。通过对比可以看到L-softmaxloss最后学到的特征之间的分离程度比原来的要明显得多。 因此L-softmaxloss的思想简单讲就是加...
This paper casts a new viewpoint on the weakness of softmax loss. 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...
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. The experiments can be run like so: ...