Summary of Deep Anomaly Detection in Text, by Andrei Manolache
Deep Anomaly Detection in Text
by Andrei Manolache
First submitted to arxiv on: 14 Dec 2023
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 Deep anomaly detection methods have seen significant advancements recently, with techniques like Stacked Autoencoders, Variational Autoencoders, and Generative Adversarial Networks significantly improving the state-of-the-art. To further boost performance, researchers are exploring self-supervised learning approaches tailored for text corpora. This thesis develops a method that leverages pretext tasks to detect anomalies in text data, achieving superior results on two benchmark datasets (20Newsgroups and AG News) for both semi-supervised and unsupervised settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomaly detection is a way to find unusual patterns in data. Some really smart methods have been developed recently, like Stacked Autoencoders and Variational Autoencoders. Researchers are now exploring new ideas called self-supervised learning. They’re trying to use these ideas to improve anomaly detection for text data. This helps us find weird or unexpected things in texts. The method they came up with worked really well on two sets of data, 20Newsgroups and AG News. |
Keywords
» Artificial intelligence » Anomaly detection » Self supervised » Semi supervised » Unsupervised
