Summary of Prosub: Probabilistic Open-set Semi-supervised Learning with Subspace-based Out-of-distribution Detection, by Erik Wallin et al.
ProSub: Probabilistic Open-Set Semi-Supervised Learning with Subspace-Based Out-of-Distribution Detection
by Erik Wallin, Lennart Svensson, Fredrik Kahl, Lars Hammarstrand
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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 The proposed framework, ProSub, for open-set semi-supervised learning (OSSL) offers a novel approach to classify data as in-distribution (ID) or out-of-distribution (OOD) by leveraging angles in feature space between data and an ID subspace. This method uses probabilistic predictions based on conditional distributions of scores given ID or OOD data, enabling more accurate OSSL. ProSub achieves state-of-the-art (SOTA) performance on several benchmark problems, providing a valuable contribution to the field. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProSub is a new way to help computers recognize when new data is unusual or part of what they already know. It works by looking at how similar the new data is to known examples and making predictions about whether it’s in or out of a certain group. This helps with a type of learning called open-set semi-supervised learning, which deals with datasets that might include unknown types of information. |
Keywords
» Artificial intelligence » Semi supervised
