Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss

• URL: http://arxiv.org/abs/2008.09880v1
• Date: Sat, 22 Aug 2020 17:13:33 GMT
• Title: Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss
• Authors: Ujjal Kr Dutta, Mehrtash Harandi and Chellu Chandra Sekhar
• Abstract summary: Existing state-of-the-art metric learning approaches require class labels to learn a metric. We propose an unsupervised approach that learns a metric without making use of class labels. The pseudo-labels are used to form triplets of examples, which guide the metric learning.
• Score: 27.955068939695042
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metric learning is an important problem in machine learning. It aims to group similar examples together. Existing state-of-the-art metric learning approaches require class labels to learn a metric. As obtaining class labels in all applications is not feasible, we propose an unsupervised approach that learns a metric without making use of class labels. The lack of class labels is compensated by obtaining pseudo-labels of data using a graph-based clustering approach. The pseudo-labels are used to form triplets of examples, which guide the metric learning. We propose a probabilistic loss that minimizes the chances of each triplet violating an angular constraint. A weight function, and an orthogonality constraint in the objective speeds up the convergence and avoids a model collapse. We also provide a stochastic formulation of our method to scale up to large-scale datasets. Our studies demonstrate the competitiveness of our approach against state-of-the-art methods. We also thoroughly study the effect of the different components of our method.

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