Summary of Rethinking Open-world Semi-supervised Learning: Distribution Mismatch and Inductive Inference, by Seongheon Park et al.
Rethinking Open-World Semi-Supervised Learning: Distribution Mismatch and Inductive Inference
by Seongheon Park, Hyuk Kwon, Kwanghoon Sohn, Kibok Lee
First submitted to arxiv on: 31 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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 research paper proposes a novel approach to open-world semi-supervised learning (OWSSL) that addresses two key limitations in current OWSSL methods. The first limitation is the assumption that labeled and unlabeled datasets share the same balanced class prior distribution, which does not hold true in many real-world applications. The second limitation is the use of unlabeled training datasets for evaluation, which may not adequately address challenges in open-world scenarios. To overcome these limitations, the authors suggest different training settings, evaluation methods, and learning strategies that are more practical and effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper aims to make computer learning systems better at handling new situations they’ve never seen before. Right now, these systems often struggle when faced with unexpected or unfamiliar data. The researchers are trying to improve the way these systems learn from both labeled and unlabeled data, so they can adapt more easily to real-world challenges. |
Keywords
» Artificial intelligence » Semi supervised
