Piecewise-Linear Manifolds for Deep Metric Learning (2024)

Bhatnagar, S. (2024). Piecewise-Linear Manifolds for Deep Metric Learning. Proceedings of Machine Learning Research, 234, 269-281.

Piecewise-Linear Manifolds for Deep Metric Learning. / Bhatnagar, Shubhang; Ahuja, Narendra.
In: Proceedings of Machine Learning Research, Vol. 234, 2024, p. 269-281.

Research output: Contribution to journal › Conference article › peer-review

Bhatnagar, S 2024, 'Piecewise-Linear Manifolds for Deep Metric Learning', Proceedings of Machine Learning Research, vol. 234, pp. 269-281.

Bhatnagar S, Ahuja N. Piecewise-Linear Manifolds for Deep Metric Learning. Proceedings of Machine Learning Research. 2024;234:269-281.

Bhatnagar, Shubhang ; Ahuja, Narendra. / Piecewise-Linear Manifolds for Deep Metric Learning. In: Proceedings of Machine Learning Research. 2024 ; Vol. 234. pp. 269-281.

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title = "Piecewise-Linear Manifolds for Deep Metric Learning",

abstract = "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 zeroshot image retrieval benchmarks.",

author = "Shubhang Bhatnagar and Narendra Ahuja",

note = "Publisher Copyright: {\textcopyright} 2024 Proceedings of Machine Learning Research; 1st Conference on Parsimony and Learning, CPAL 2024 ; Conference date: 03-01-2024 Through 06-01-2024",

year = "2024",

language = "English (US)",

volume = "234",

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T1 - Piecewise-Linear Manifolds for Deep Metric Learning

AU - Bhatnagar, Shubhang

AU - Ahuja, Narendra

N1 - Publisher Copyright:© 2024 Proceedings of Machine Learning Research

PY - 2024

Y1 - 2024

N2 - 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 zeroshot image retrieval benchmarks.

AB - 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 zeroshot image retrieval benchmarks.

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