Self-Supervised Deep Metric Learning for Pointsets

Pattaramanee Arsomngern, Cheng Long, Supasorn Suwajanakorn, Sarana Nutanong

Abstract: Deep metric learning is a supervised learning paradigm to construct a meaningful vector space to represent complex objects. A successful application of deep metric learning to pointsets means that we can avoid expensive retrieval operations on objects such as documents and can significantly facilitate many machine learning and data mining tasks involving pointsets. We propose a self-supervised deep metric learning solution for pointsets. The novelty of our proposed solution lies in a self-supervision mechanism that makes use of a distribution distance for set ranking called the Earth's Mover Distance (EMD) to generate pseudo labels. Our experimental studies on four documents datasets show that our proposed solutions outperform baselines and state-of-the-art approaches on unsupervised deep metric learning in most settings.