Summary of Deep Evolving Semi-supervised Anomaly Detection, by Jack Belham et al.
Deep evolving semi-supervised anomaly detection
by Jack Belham, Aryan Bhosale, Samrat Mukherjee, Biplab Banerjee, Fabio Cuzzolin
First submitted to arxiv on: 1 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper formalizes the task of continual semi-supervised anomaly detection (CSAD) by introducing a baseline model based on variational autoencoders (VAEs) and deep generative replay with outlier rejection. The authors highlight the importance of this problem formulation, which mimics real-world conditions, and demonstrate promising results using extreme value theory (EVT). The study explores the effects of varying amounts of labeled and unlabeled data, as well as their location in the data stream. The results show that outlier rejection can often surpass baseline methods, such as Elastic Weight Consolidation (EWC). The paper provides a baseline for CSAD and suggests future research directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at finding unusual things in data without being told what to look for every time. This helps with real-world problems where data keeps coming in and we need to find new anomalies quickly. The authors created a special kind of computer model that can learn from both labeled and unlabeled data, which makes it more like how humans learn. They tested this model and found that it works well even when there’s not much labeled data or when the unusual things are hidden in the data stream. This is important because it could help us find new medical conditions earlier or detect cyber attacks faster. |
Keywords
» Artificial intelligence » Anomaly detection » Semi supervised
