Potential Field Based Deep Metric Learning

• URL: http://arxiv.org/abs/2405.18560v2
• Date: Sun, 01 Dec 2024 05:22:22 GMT
• Title: Potential Field Based Deep Metric Learning
• Authors: Shubhang Bhatnagar, Narendra Ahuja,
• Abstract summary: Deep metric learning involves training a network to learn a semantically meaningful representation space. We present a novel, compositional DML model inspired by electrostatic fields in physics. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise.
• Score: 8.670873561640903
• License:
• 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, inspired by electrostatic fields in physics 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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