contrastive loss

triplet loss

• triplet loss为什么通常L2 normalize：1.避免只是对embedding做scaling 2.确定了L2 distance范围，方便选择margin

In triplet loss training, a triplet contains two images belonging to the same class, referred to as the anchor and positive samples, and a third image, from a different class, which is referred to as the negative sample. The triplet loss function is given as, $[d(a, p) - d(a, n) + m]+$, where $a$, $p$ and $n$ are anchor, positive, and negative samples, respectively. $d(\cdot,\cdot)$ is the learned metric function and $m$ is a margin term which encourages the negative sample to be further from the anchor than the positive sample.

NormSoftmax loss

Sampling

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作者: Rowl1ng