Summary of Toward a Realistic Benchmark For Out-of-distribution Detection, by Pietro Recalcati et al.
Toward a Realistic Benchmark for Out-of-Distribution Detection
by Pietro Recalcati, Fabio Garcea, Luca Piano, Fabrizio Lamberti, Lia Morra
First submitted to arxiv on: 16 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 Deep neural networks are widely used in various technologies but remain vulnerable to out-of-distribution (OOD) samples. To address this issue, researchers often equip these networks with the ability to detect OOD samples. Several benchmarks have been proposed for designing and validating OOD detection techniques, but most of them rely on far-OOD samples from distinct distributions, lacking the complexity needed to capture real-world scenarios. In this work, a comprehensive benchmark is introduced, based on ImageNet and Places365, which assigns individual classes as in-distribution or out-of-distribution depending on semantic similarity with the training set. The benchmark’s properties can be modified by using different techniques to determine which classes are considered in-distribution. Experimental results show that the measured efficacy of OOD detection techniques depends on the selected benchmark and that confidence-based methods may perform better than classifier-based ones for near-OOD samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep neural networks are very good at recognizing things, but they can get confused when shown pictures or data that is different from what they were trained on. To fix this problem, researchers try to teach the networks how to recognize when something is unusual. There are several ways to test these methods, but most of them use really easy and really hard examples, which doesn’t reflect real life. In this research, a new way to test these methods is introduced, using a mix of easy and hard examples from images and places. The results show that the performance of these methods depends on the type of test used and that some methods are better than others at recognizing when something is unusual. |
