Potential Field Based Deep Metric Learning
<|reference_start|>Potential Field Based Deep Metric Learning: 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.<|reference_end|>
@article{bhatnagar2024potential,
    title={Potential Field Based Deep Metric Learning},
    author={Shubhang Bhatnagar, Narendra Ahuja},
    journal={arXiv preprint arXiv:2405.18560},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.18560},
    primaryClass={cs.CV cs.AI cs.IR cs.LG eess.IV}
}