Summary of Fodfom: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-distribution Detector, By Jiankang Chen and Ling Deng and Zhiyong Gan and Wei-shi Zheng and Ruixuan Wang
FodFoM: Fake Outlier Data by Foundation Models Creates Stronger Visual Out-of-Distribution Detector
by Jiankang Chen, Ling Deng, Zhiyong Gan, Wei-Shi Zheng, Ruixuan Wang
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 A critical problem in deploying machine learning models is detecting out-of-distribution (OOD) data. The main challenge is mitigating overconfidence on OOD data. Recent methods, such as using auxiliary datasets or generating synthetic outliers, show promise but are limited by costly data collection or simplified assumptions. This paper proposes FodFoM, a novel framework that leverages multiple foundation models to generate two types of challenging fake outlier images for classifier training. By utilizing these innovative techniques, FodFoM aims to improve OOD detection performance and enhance the reliability of machine learning-based applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine using a computer program to make decisions, but sometimes it makes mistakes because it doesn’t understand what’s outside its normal range of data. This is called out-of-distribution (OOD) detection. In this paper, researchers propose a new way to improve OOD detection by combining different types of fake images that are designed to be tricky for the program to recognize. The goal is to make the program better at detecting when it’s outside its normal range, so we can trust the decisions it makes. |
Keywords
* Artificial intelligence * Machine learning
