Summary of A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels Is Critical For Semi-supervised Classification, by Jiaqi Wu et al.
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
by Jiaqi Wu, Junbiao Pang, Baochang Zhang, Qingming Huang
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed lightweight channel-based ensemble method consolidates multiple inferior pseudo-labels into a theoretically guaranteed unbiased and low-variance one, addressing the issue of biased and high-variance predictions in semi-supervised learning (SSL) when only a little labeled data are supplied. This approach can be extended to any SSL framework, such as FixMatch or FreeMatch, and outperforms state-of-the-art techniques on CIFAR10/100 in terms of effectiveness and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Semi-supervised learning is a way for computers to learn from some labeled data and a lot of unlabeled data. Right now, the best methods use something called pseudo-labels, which are predictions made by the computer about the unlabeled data. These pseudo-labels are used to train the model again, but they can be biased or not very accurate. The new method proposed in this paper is a way to combine many of these pseudo-labels into one that’s more accurate and unbiased. This makes it easier for computers to learn from both labeled and unlabeled data. |
Keywords
» Artificial intelligence » Semi supervised
