Summary of Detecting and Learning Out-of-distribution Data in the Open World: Algorithm and Theory, by Yiyou Sun
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory
by Yiyou Sun
First submitted to arxiv on: 10 Oct 2023
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 The proposed thesis tackles the challenges of traditional machine learning models in open-world scenarios where systems encounter novel data and contexts. By developing Out-of-distribution (OOD) Detection and Open-world Representation Learning (ORL), this research aims to create more robust and reliable AI systems that can adapt to dynamic changes in real-world applications. Specifically, OOD detection focuses on identifying unknown instances that deviate from the training distribution, reducing confident errors about unfamiliar inputs. Building upon this foundation, ORL enables the model not only to detect but also learn from new classes, thereby expanding its capabilities and performance. This thesis contributes both algorithmic solutions and theoretical foundations for open-world machine learning, ultimately paving the way for developing AI systems that can thrive in ever-changing environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The thesis focuses on helping machines learn in situations where they encounter new information or things they’ve never seen before. Currently, many AI models are trained to recognize specific types of data, but they often struggle when faced with unfamiliar inputs. To overcome this challenge, the researchers developed two important tools: one that detects when something is outside what the model has learned, and another that allows the model to learn from and understand new information. This research helps machines become more reliable and capable in real-world situations. |
Keywords
* Artificial intelligence * Machine learning * Representation learning
