Piecewise-Linear Manifolds for Deep Metric Learning
<|reference_start|>Piecewise-Linear Manifolds for Deep Metric Learning: Unsupervised deep metric learning (UDML) focuses on learning a semantic representation space using only unlabeled data. This challenging problem requires accurately estimating the similarity between data points, which is used to supervise a deep network. For this purpose, we propose to model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data manifold in a small neighborhood of a point. These neighborhoods are used to estimate similarity between data points. We empirically show that this similarity estimate correlates better with the ground truth than the similarity estimates of current state-of-the-art techniques. We also show that proxies, commonly used in supervised metric learning, can be used to model the piecewise-linear manifold in an unsupervised setting, helping improve performance. Our method outperforms existing unsupervised metric learning approaches on standard zero-shot image retrieval benchmarks.<|reference_end|>
@article{bhatnagar2024piecewise-linear,
    title={Piecewise-Linear Manifolds for Deep Metric Learning},
    author={Shubhang Bhatnagar and Narendra Ahuja},
    journal={arXiv preprint arXiv:2403.14977},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14977},
    primaryClass={cs.CV cs.AI cs.LG eess.IV}
}