Multi-level Distance Regularization for Deep Metric Learning
This work provides an incremental improvement for deep metric learning practitioners seeking better generalization and performance on existing benchmarks.
This paper introduces Multi-level Distance Regularization (MDR), a method that regularizes pairwise distances between embedding vectors into multiple similarity levels. When combined with a simple Triplet loss, MDR achieves state-of-the-art performance on benchmark datasets including CUB-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval.
We propose a novel distance-based regularization method for deep metric learning called Multi-level Distance Regularization (MDR). MDR explicitly disturbs a learning procedure by regularizing pairwise distances between embedding vectors into multiple levels that represents a degree of similarity between a pair. In the training stage, the model is trained with both MDR and an existing loss function of deep metric learning, simultaneously; the two losses interfere with the objective of each other, and it makes the learning process difficult. Moreover, MDR prevents some examples from being ignored or overly influenced in the learning process. These allow the parameters of the embedding network to be settle on a local optima with better generalization. Without bells and whistles, MDR with simple Triplet loss achieves the-state-of-the-art performance in various benchmark datasets: CUB-200-2011, Cars-196, Stanford Online Products, and In-Shop Clothes Retrieval. We extensively perform ablation studies on its behaviors to show the effectiveness of MDR. By easily adopting our MDR, the previous approaches can be improved in performance and generalization ability.