Given a similarity metric, contrastive methods learn a representation in
which examples that are similar are pushed together and examples that are
dissimilar are pulled apart. Contrastive learning techniques have been utilized
extensively to learn representations for tasks ranging from image
classification to caption generation. However, existing contrastive learning
approaches can fail to generalize because they do not take into account the
possibility of different similarity relations. In this paper, we propose a
novel multi-similarity contrastive loss (MSCon), that learns generalizable
embeddings by jointly utilizing supervision from multiple metrics of
similarity. Our method automatically learns contrastive similarity weightings
based on the uncertainty in the corresponding similarity, down-weighting
uncertain tasks and leading to better out-of-domain generalization to new
tasks. We show empirically that networks trained with MSCon outperform
state-of-the-art baselines on in-domain and out-of-domain settings.

基于多个相似性度量的新型多相似性对比损失（MSCon）提供了在不同相似关系下学习可推广嵌入的方法，通过自动学习对应相似性的对比权重，降低不确定任务的影响，从而在内部和外部领域中优于最先进的基准模型。