Deep Metric Learning via Adaptive Learnable Assessment

In this paper, we propose a deep metric learning via adaptive learnable assessment (DML-ALA) method for image retrieval and clustering, which aims to learn a sample assessment strategy to maximize the generalization of the trained metric. Unlike existing deep metric learning methods that usually utilize a fixed sampling strategy like hard negative mining, we propose a sequence-aware learnable assessor which re-weights each training example to train the metric towards good generalization. We formulate the learning of this assessor as a meta-learning problem, where we employ an episode-based training scheme and update the assessor at each iteration to adapt to the current model status. We construct each episode by sampling two subsets of disjoint labels to simulate the procedure of training and testing and use the performance of one-gradient-updated metric on the validation subset as the meta-objective of the assessor. Experimental results on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the effectiveness of the proposed approach.