A common practice in metric learning is to train and test an embedding model
for each dataset. This dataset-specific approach fails to simulate real-world
scenarios that involve multiple heterogeneous distributions of data. In this
regard, we introduce a novel metric learning paradigm, called Universal Metric
Learning (UML), which learns a unified distance metric capable of capturing
relations across multiple data distributions. UML presents new challenges, such
as imbalanced data distribution and bias towards dominant distributions. To
address these challenges, we propose Parameter-efficient Universal Metric
leArning (PUMA), which consists of a pre-trained frozen model and two
additional modules, stochastic adapter and prompt pool. These modules enable to
capture dataset-specific knowledge while avoiding bias towards dominant
distributions. Additionally, we compile a new universal metric learning
benchmark with a total of 8 different datasets. PUMA outperformed the
state-of-the-art dataset-specific models while using about 69 times fewer
trainable parameters.

我们引入了一种名为通用度量学习（UML）的新型度量学习范式，它能够学习到能够捕捉多个数据分布关系的统一距离度量。通过使用预训练模型和两个额外的模块，即随机适配器和提示池，我们提出了一种名为参数高效通用度量学习（PUMA）的方法来解决不平衡数据分布和对主导分布的偏见等新挑战。此外，我们还编译了一个包含 8 个不同数据集的新通用度量学习基准。相较于特定数据集模型，PUMA 在使用大约 69 倍较少的可训练参数的情况下表现更好。