Understanding Hyperbolic Metric Learning through Hard Negative Sampling

arXiv:2404.15523v1 Announce Type: new
Abstract: In recent years, there has been a growing trend of incorporating hyperbolic geometry methods into computer vision. While these methods have achieved state-of-the-art performance on various metric learning tasks using hyperbolic distance measurements, the underlying theoretical analysis supporting this superior performance remains under-exploited. In this study, we investigate the effects of integrating hyperbolic space into metric learning, particularly when training with contrastive loss. We identify a need for a comprehensive comparison between Euclidean and hyperbolic …

arxiv cs.cv negative sampling through type understanding