Uncertainty-Guided Metric Learning Without Labels

Abstract

Unsupervised metric learning aims to learn the discriminative representations by grouping similar examples in the absence of labels. Many unsupervised metric learning algorithms combine clustering-based pseudo-label generation with embedding fine-tuning. However pseudo-labels can be unreliable and noisy. This could affect metric learning and degrade the quality of the learned representations. In this work we propose an approach to reduce the negative effect of label noise on learning discriminative embeddings by using context and prediction uncertainty. In particular we refine the pseudo-labels by aggregating information from neighbors. We propose a function to weigh the pairs leveraging their prediction confidence and uncertainty. We modify the metric learning loss function to incorporate this weight. Experimental results demonstrate the effectiveness of our proposed method on standard datasets for metric learning.