然后我们可以知道Softmax损失中的 \theta_{1}^{'} 比L-Softmax中的 \theta_{1} 大m-1 倍。结果,特征和 W_{1} 之间的角度将变小。对于每个类别,同样的结论成立。本质上,L-Softmax使每个类的可行角度1变窄,并在这些类之间生成margin 对于||W_{1}||>||W_{2}|| 和||W_{1}||<||W_{2}||...
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...
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...
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...
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...
To overcome this limitation, we propose a Combined Angular Margin and Cosine Margin Softmax Loss (AMCM-Softmax) approach in this paper to enhance intra-class compactness and inter-class discrepancy simultaneously. Specifically, normalization on the weight vectors and feature vectors is adopted to ...
mx-lsoftmax 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...
L-Softmax loss is the combination of "LargeMarginInnerProduct" layer and "SoftmaxWithLoss" layer. If the type of the layer is SINGLE/DOUBLE/TRIPLE/QUADRUPLE, then m is set as 1/2/3/4 respectively. mnist example can be run directly after compilation. cifar10 and cifar10+ requires ...
然后在角度θ (groundtrouth)上加上一个额外的角度m得到 θ+m (m为加的惩罚项),接着计算cos函数得到 cos(θ+m),再将所有的log乘以特征尺度s,进行re-scale 得到 s*cos(θ+m),然后将log送到softmax函数中。再用Ground Truth和One Hot Vector一起算出交叉熵损失。