Summary of A Review Of Pseudo-labeling For Computer Vision, by Patrick Kage et al.
A Review of Pseudo-Labeling for Computer Vision
by Patrick Kage, Jay C. Rothenberger, Pavlos Andreadis, Dimitrios I. Diochnos
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Deep neural networks have excelled in various computer science applications, particularly in computer vision. However, they often require substantial labeled datasets to generalize well. Semi-supervised learning is an active area of research, aiming to utilize large unlabeled sample sets instead. Pseudo-labeling is a method that assigns labels to unlabeled samples using model outputs, treating these assigned labels as pseudo-labels for training. This work explores the broader implications of pseudo-labels within self-supervised and unsupervised learning methods. By connecting these areas, we identify potential directions where advancements in one area can benefit others, such as curriculum learning and self-supervised regularization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make deep neural networks work better with small amounts of labeled data. Currently, these networks need a lot of labeled data to be useful. But what if we could use lots of unlabeled data instead? This is called semi-supervised learning. The authors are exploring ways to assign labels to this unlabeled data using the network’s own predictions. By doing so, they can train the network more effectively and make it learn faster. They also discuss how advancements in one area can benefit others. |
Keywords
» Artificial intelligence » Curriculum learning » Regularization » Self supervised » Semi supervised » Unsupervised
