Summary of Piecewise-linear Manifolds For Deep Metric Learning, by Shubhang Bhatnagar and Narendra Ahuja
Piecewise-Linear Manifolds for Deep Metric Learning
by Shubhang Bhatnagar, Narendra Ahuja
First submitted to arxiv on: 22 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to unsupervised deep metric learning (UDML) that accurately estimates the similarity between data points using only unlabeled data. The authors model the high-dimensional data manifold using a piecewise-linear approximation, with each low-dimensional linear piece approximating the data in a small neighborhood of a point. This similarity estimate is shown to correlate better with the ground truth than current state-of-the-art techniques. Additionally, proxies used in supervised metric learning can be applied in an unsupervised setting, improving performance. The method outperforms existing UDML approaches on standard zero-shot image retrieval benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use computers to understand how similar or different things are without any labels or clues. Right now, this kind of learning is really hard because we need lots of labeled data to make it work. But the authors have come up with an idea that doesn’t require any labels! They’re using something called piecewise-linear approximation to create a special map of where all the similar things are in high-dimensional space. This map helps computers figure out how similar or different things are, and it’s way better than what other methods can do right now. |
Keywords
* Artificial intelligence * Supervised * Unsupervised * Zero shot
