Summary of Can I Trust My Anomaly Detection System? a Case Study Based on Explainable Ai, by Muhammad Rashid et al.
Can I trust my anomaly detection system? A case study based on explainable AI
by Muhammad Rashid, Elvio Amparore, Enrico Ferrari, Damiano Verda
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 investigates the efficacy of variational autoencoder (VAE)-based generative models for detecting anomalies in images in a semi-supervised setting. A popular approach uses anomaly scores computed from reconstruction disparities, achieving high accuracy on benchmark datasets. However, this method may overlook spurious features, raising concerns about its actual effectiveness. The study employs explainable AI methods to evaluate the robustness of VAE-based anomaly detection systems and sheds light on their real-world performance. By analyzing different perspectives, the research reveals that many samples are misclassified as anomalous due to irrelevant or misleading factors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special kind of computer program that can look at pictures and find weird parts in them. This is called anomaly detection, and it’s important because sometimes machines need help identifying things that don’t belong. Researchers have been using a type of computer model called variational autoencoders to do this, but they’re not sure if these models are actually good at finding the right kinds of weirdness. In this study, scientists used special tools to understand how well these models work and found out that sometimes they mistake things for being weird when they shouldn’t be. |
Keywords
» Artificial intelligence » Anomaly detection » Semi supervised » Variational autoencoder
