Summary of Dynamic Against Dynamic: An Open-set Self-learning Framework, by Haifeng Yang et al.
Dynamic Against Dynamic: An Open-set Self-learning Framework
by Haifeng Yang, Chuanxing Geng, Pong C. Yuen, Songcan Chen
First submitted to arxiv on: 27 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 open-set self-learning (OSSL) framework develops a novel dynamic approach against a dynamic changing open-set world, starting with a good closed-set classifier trained by known classes. The OSSL framework adapts to changing data distributions during testing using available test samples, allowing for the rejection of unknown class samples and their utilization as the instantiated representation of unknown classes. A self-matching module is designed to achieve adaptation in identifying known class samples while rejecting unknown class samples, enhancing the model’s discriminability. This approach sets new performance milestones in various standard and cross-data benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists develop a new way for computers to recognize things they don’t know how to classify yet. Currently, computer programs learn to say “I don’t know” by looking at what they do know. But this doesn’t work well when the unknown things can appear anywhere in the data. The researchers propose a new approach that adapts to changing situations during testing and uses unknown samples to make the program better. This method sets new records in recognizing things it has never seen before. |
