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[P] Benchmarking Metric Learning Algorithms the Right Way

I’ve been researching metric learning algorithms for a while now, and in the process I discovered some issues with the field.

You can read about it here: https://medium.com/@tkm45/benchmarking-metric-learning-algorithms-the-right-way-90c073a83968

TL;DR:

  1. Many papers don’t do apple-to-apple comparisons. They change the network architecture, embedding size, data augmentation, or just use performance-boosting tricks that aren’t mentioned in their paper.
  2. Most papers don’t use a validation set.
  3. Two baseline algorithms (triplet and contrastive loss) are actually competitive with the state-of-the-art, but are not presented this way in most papers.
  4. I’ve made a flexible benchmarking tool that can standardize the way we evaluate metric learning algorithms. You can see it here: https://github.com/KevinMusgrave/powerful_benchmarker

submitted by /u/VanillaCashew
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.