目录

16-NIPS-improved deep metric learning with multi-class n-pair loss objective

Multiple Negative Examples

(N+1)-tuplet loss

multi-class N-pair loss (N-pair-mc)

one-vs-one N-pair loss (N-pair-ovo)

Hard negative class mining

L2 norm regularization of embedding vectors

Experimental Results

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16-NIPS-improved deep metric learning with multi-class n-pair loss objective

Multiple Negative Examples

 

(N+1)-tuplet loss

同时推开多个类的负样本

 N=2 类似triplet loss

（4）（5）

SoftPlus 激活函数：

近似relu函数

不包含0，解决了 Dead ReLU 问题，但不包含负区间，不能加速学习

 N=L 类似softmax loss

multi-class N-pair loss (N-pair-mc)

one-vs-one N-pair loss (N-pair-ovo)

Hard negative class mining

L2 norm regularization of embedding vectors

排除范数的影响——>归一化

——>严格限制|fTf+|<1，优化困难——>正则化嵌入向量的L2范数让它小

 for anchor, positive, negative_set in zip(anchors, positives, negatives):
        a_embs, p_embs, n_embs = batch[anchor:anchor+1], batch[positive:positive+1], batch[negative_set]
        inner_sum = a_embs[:,None,:].bmm((n_embs - p_embs[:,None,:]).permute(0,2,1))
        inner_sum = inner_sum.view(inner_sum.shape[0], inner_sum.shape[-1])
        loss  = loss + torch.mean(torch.log(torch.sum(torch.exp(inner_sum), dim=1) + 1))/len(anchors)
        loss  = loss + self.l2_weight*torch.mean(torch.norm(batch, p=2, dim=1))/len(anchors)

Experimental Results

adapt the smooth upper bound of triplet loss in Equation (4) instead of large-margin formulation  in all our experiments to be consistent with N-pair-mc losses.

multi-class N-pair loss表现更好：one-vs-one N-pair loss解耦，每个负样本损失都是独立的

 在固定batch size下，每个类采集一对样本，样本来自的类变多。训练涉及的负类越多越好。