Summary of Improving Self-training Under Distribution Shifts Via Anchored Confidence with Theoretical Guarantees, by Taejong Joo et al.
Improving self-training under distribution shifts via anchored confidence with theoretical guarantees
by Taejong Joo, Diego Klabjan
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper tackles the issue of self-training under distribution shifts, where existing methods often struggle due to increased discrepancies between prediction confidence and actual accuracy. The authors develop a principled approach based on temporal consistency, which builds an uncertainty-aware temporal ensemble with simple relative thresholding. This ensemble smooths noisy pseudo labels to promote selective temporal consistency. The paper shows that this method is asymptotically correct and reduces the optimality gap of self-training. Experimental results demonstrate consistent performance improvements (8%-16%) across diverse scenarios without a computational overhead, along with improved calibration and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making machines learn better when their environment changes suddenly. Right now, these machines often get confused because they’re so sure they’re right, but they’re actually wrong. The authors came up with a clever way to help them by looking at how they changed over time. They create a special kind of team that works together to make decisions and smooth out mistakes. This team helps the machine learn better and makes fewer mistakes. The authors tested their idea on many different scenarios and found it worked really well, improving the machine’s performance without using too much computer power. |
Keywords
» Artificial intelligence » Self training
