Sharing Matters for Generalization in Deep Metric Learning
- URL: http://arxiv.org/abs/2004.05582v3
- Date: Thu, 9 Sep 2021 08:02:54 GMT
- Title: Sharing Matters for Generalization in Deep Metric Learning
- Authors: Timo Milbich and Karsten Roth and Biagio Brattoli and Bj\"orn Ommer
- Abstract summary: This work investigates how to learn characteristics that separate between classes without the need for annotations or training data.
By formulating our approach as a novel triplet sampling strategy, it can be easily applied on top of recent ranking loss frameworks.
- Score: 22.243744691711452
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning the similarity between images constitutes the foundation for
numerous vision tasks. The common paradigm is discriminative metric learning,
which seeks an embedding that separates different training classes. However,
the main challenge is to learn a metric that not only generalizes from training
to novel, but related, test samples. It should also transfer to different
object classes. So what complementary information is missed by the
discriminative paradigm? Besides finding characteristics that separate between
classes, we also need them to likely occur in novel categories, which is
indicated if they are shared across training classes. This work investigates
how to learn such characteristics without the need for extra annotations or
training data. By formulating our approach as a novel triplet sampling
strategy, it can be easily applied on top of recent ranking loss frameworks.
Experiments show that, independent of the underlying network architecture and
the specific ranking loss, our approach significantly improves performance in
deep metric learning, leading to new the state-of-the-art results on various
standard benchmark datasets. Preliminary early access page can be found here:
https://ieeexplore.ieee.org/document/9141449
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