Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective
This work provides theoretical insights into self-supervised metric learning for improving distance-based tasks, but it is incremental as it builds on existing methods without introducing a new paradigm.
The authors developed a statistical framework to analyze how self-supervised metric learning benefits downstream tasks in multi-view data, showing that the learned distance has desirable properties and characterizing improvements in tasks like k-means clustering and k-nearest neighbor classification, with theoretical bounds on sample requirements and experimental validation.
Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction's weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, $k$-means clustering, and $k$-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation's accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the paper.