Summary of On the Detection Of Anomalous or Out-of-distribution Data in Vision Models Using Statistical Techniques, by Laura O’mahony et al.
On the Detection of Anomalous or Out-Of-Distribution Data in Vision Models Using Statistical Techniques
by Laura O’Mahony, David JP O’Sullivan, Nikola S. Nikolov
First submitted to arxiv on: 21 Mar 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 This paper explores the vulnerability of machine learning systems to out-of-distribution data and anomalous inputs, which can lead to incorrect predictions. To address this issue, researchers assess Benford’s law as a method for detecting atypical inputs. The goal is to use Benford’s law as a filter for anomalous data points and signal out-of-distribution data. The paper aims to open up new avenues for applying this technique in various settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning systems can be tricked into making mistakes by using unusual or fake data. This is a big problem because machine learning models are used with all sorts of different data. Detecting when something is wrong is crucial. One way to do this is by looking at the patterns in the data, like how often certain numbers appear. The researchers think that using these patterns could help filter out bad data and warn us when things don’t look right. |
Keywords
* Artificial intelligence * Machine learning
