论文标题
连续直方图丢失：超出神经相似性
Continuous Histogram Loss: Beyond Neural Similarity
论文作者
论文摘要
近年来，从研究中获得了相似性学习引起了很多关注，最近提出了许多成功的方法。但是，大多数最新的相似性学习方法仅考虑二进制相似性。在本文中，我们介绍了一种称为连续直方图损耗（CHL）的新损失函数，该函数将最近提出的直方图丢失推广到多价值相似性，即允许可接受的相似性值在某个范围内连续分布。新颖的损失函数是通过以可区分的方式将成对距离和相似性汇总为2D直方图的，然后计算条件概率随着相似性的增加不会降低的概率。新颖的损失能够解决更广泛的任务，包括相似性学习，表示学习和数据可视化。
Similarity learning has gained a lot of attention from researches in recent years and tons of successful approaches have been recently proposed. However, the majority of the state-of-the-art similarity learning methods consider only a binary similarity. In this paper we introduce a new loss function called Continuous Histogram Loss (CHL) which generalizes recently proposed Histogram loss to multiple-valued similarities, i.e. allowing the acceptable values of similarity to be continuously distributed within some range. The novel loss function is computed by aggregating pairwise distances and similarities into 2D histograms in a differentiable manner and then computing the probability of condition that pairwise distances will not decrease as the similarities increase. The novel loss is capable of solving a wider range of tasks including similarity learning, representation learning and data visualization.
