It Takes Two to Tango: Mixup for Deep Metric Learning
- URL: http://arxiv.org/abs/2106.04990v1
- Date: Wed, 9 Jun 2021 11:20:03 GMT
- Title: It Takes Two to Tango: Mixup for Deep Metric Learning
- Authors: Shashanka Venkataramanan, Bill Psomas, Yannis Avrithis, Ewa Kijak,
Laurent Amsaleg, Konstantinos Karantzalos
- Abstract summary: State-of-the-art methods focus mostly on sophisticated loss functions or mining strategies.
Mixup is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
We show that mixing inputs, intermediate representations or embeddings along with target labels significantly improves representations and outperforms state-of-the-art metric learning methods on four benchmark datasets.
- Score: 16.60855728302127
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Metric learning involves learning a discriminative representation such that
embeddings of similar classes are encouraged to be close, while embeddings of
dissimilar classes are pushed far apart. State-of-the-art methods focus mostly
on sophisticated loss functions or mining strategies. On the one hand, metric
learning losses consider two or more examples at a time. On the other hand,
modern data augmentation methods for classification consider two or more
examples at a time. The combination of the two ideas is under-studied.
In this work, we aim to bridge this gap and improve representations using
mixup, which is a powerful data augmentation approach interpolating two or more
examples and corresponding target labels at a time. This task is challenging
because, unlike classification, the loss functions used in metric learning are
not additive over examples, so the idea of interpolating target labels is not
straightforward. To the best of our knowledge, we are the first to investigate
mixing examples and target labels for deep metric learning. We develop a
generalized formulation that encompasses existing metric learning loss
functions and modify it to accommodate for mixup, introducing Metric Mix, or
Metrix. We show that mixing inputs, intermediate representations or embeddings
along with target labels significantly improves representations and outperforms
state-of-the-art metric learning methods on four benchmark datasets.
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