Summary of Eol: Transductive Few-shot Open-set Recognition by Enhancing Outlier Logits, By Mateusz Ochal and Massimiliano Patacchiola and Malik Boudiaf and Sen Wang
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier Logits
by Mateusz Ochal, Massimiliano Patacchiola, Malik Boudiaf, Sen Wang
First submitted to arxiv on: 4 Aug 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 The paper explores Open-Set Few-Shot Recognition (OSFSL), a more challenging variation of traditional few-shot learning that involves recognizing unseen objects from a query set with unknown classes. To tackle this problem, the authors propose an Enhanced Outlier Logit (EOL) method that leverages InfoMax principle and transductive inference to refine class prototype representations. The EOL method calibrates model predictions through pseudo-label accuracy improvement and optimization objective refinement. Experimental results show significant performance improvements ranging from 1.3% to 6.3% across various classification and outlier detection metrics and benchmarks, even in the presence of imbalance between known and unknown classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about teaching computers to recognize new things they haven’t seen before, using just a few examples. This is called Open-Set Few-Shot Recognition (OSFSL). The challenge is that some of these new things might not belong to any class we know. To solve this problem, the authors created a special method called Enhanced Outlier Logit (EOL) that helps computers better understand what they’re seeing and make accurate decisions. |
Keywords
» Artificial intelligence » Classification » Few shot » Inference » Optimization » Outlier detection
