Potential Field Based Deep Metric Learning

Shubhang Bhatnagar; Narendra Ahuja

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arXiv · 2405.18560

Potential Field Based Deep Metric Learning

Shubhang Bhatnagar·Narendra Ahuja

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Abstract

Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model that instead of in tuples, represents the influence of each example (embedding) by a continuous potential field, and superposes the fields to obtain their combined global potential field. We use attractive/repulsive potential fields to represent interactions among embeddings from images of the same/different classes. Contrary to typical learning methods, where mutual influence of samples is proportional to their distance, we enforce reduction in such influence with distance, leading to a decaying field. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise. Like other proxy-based methods, we also use proxies to succinctly represent sub-populations of examples. We evaluate our method on three standard DML benchmarks- Cars-196, CUB-200-2011, and SOP datasets where it outperforms state-of-the-art baselines.

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BibTeX RIS

Shubhang Bhatnagar, Narendra Ahuja. 2025-04-19. Potential Field Based Deep Metric Learning. https://arxiv.org/abs/2405.18560

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