Angular margin-based softmax lossT-vMF similarityIntra-class feature distributionDeep learningRegularizationImbalanced dataTo enhance the accuracy of breast cancer diagnosis, current practices rely on biopsies and microscopic examinations. However, this approach is known for being time-consuming, tedious, ...
本文来自《L2-constrained Softmax Loss for Discriminative Face Verification》,时间线为2017年6月。 近些年,人脸验证的性能因引入了深度卷积网络而提升很大。一个典型的人脸识别流程就是: 训练一个基于softmax loss的深度卷积网络; 提取倒数第二层作为每个样本图片的特征表征; 基于两个样本的表征向量,计算cos的向量...
Angular Margin系列损失函数:L-Softmax Loss、A-Softmax Loss、CosFace Loss、ArcFace Loss 1 L-Softmax loss L-Softmax loss由北大Weiyang Liu等在2016.12发表,将Softmax Loss转换到余弦空间,对角度乘以决策余…
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: python train_fMNIST.py --num-epochs...
Euclidean Based Margin: Contrastive Loss Triple Loss Angular Based Margin: L-Softmax A-Softmax 一个思想: 通过加入一些限制,获取剔除一些衡量标准,能够很好的压缩类内的距离和加大类间的距离 比如L-Softmax 到 A-Softmax, 剔除了幅度的衡量,完全由角度来区分类别,更好的压缩了类内的距离,加大了类间的距离...
There are a simple set of experiments on Fashion-MNIST [2] included in train_fMNIST.py which 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: python train_fMNIST.py --num...
Sphereface引入了angular margin的重要思想,但它的损失函数需要一系列的近似才能被计算,这导致了网络训练不稳定,为了稳定训练,提出了一种hybrid loss函数,也包含标准的softmax loss,根据经验,softmax loss主导着训练过程,因为the integer-based multiplicative angular margin使得target logit curve非常陡峭,从而阻碍收敛。Cos...
In order to stabilise train- ing, they proposed a hybrid loss function which includes the standard softmax loss. Empirically, the softmax loss dom- inates the training process, because the integer-based mul- tiplicative angular margin makes the target logit curve ...
However, train- ing AM-Softmax and AAM-Softmax have proven to be challenging because they are sensitive to the scale and the marginal value of the loss function. To improve the perfor- mance of AM-Softmax loss, Zhou et al. [22] proposed to dynamically set the margin of each training ...
可以看到,origin softmax是没有将weights(分类权重)归一化的,这个我在之前的project(loss functions)中也分析过了,没有将weights归一化,那就永远属于欧式距离的范围。所以,只有将weights归一化,才能称之为cosine距离。 然后是这张图,sphereface的核心思想之二:margin(论文中): ...