Moving in the Right Direction: A Regularization for Deep Metric Learning

Abstract

Deep metric learning leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminable embedding space. While effective sampling of pairs is critical for shaping the metric space during training, the relative interactions between pairs, and consequently the forces exerted on these pairs that direct their displacement in the embedding space can significantly impact the formation of well separated clusters. In this work, we identify a shortcoming of existing loss formulations which fail to consider more optimal directions of pair displacements as another criterion for optimization. We propose a novel direction regularization to explicitly account for the layout of sampled pairs and attempt to introduce orthogonality in the representations. The proposed regularization is easily integrated into existing loss functions providing considerable performance improvements. We experimentally validate our hypothesis on the Cars-196, CUB-200 and InShop datasets and outperform existing methods to yield state-of-the-art results on these datasets.