Self-Supervised Losses for One-Class Textual Anomaly Detection

12 April 2022

Papers citing "Self-Supervised Losses for One-Class Textual Anomaly Detection"

9 / 9 papers shown

Title
LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models Huiqiang Jiang Qianhui Wu Chin-Yew Lin Yuqing Yang Lili Qiu 40 102 0 09 Oct 2023
Understanding the limitations of self-supervised learning for tabular anomaly detection Kimberly T. Mai Toby O. Davies Lewis D. Griffin SSL 32 0 0 15 Sep 2023
Few-shot Anomaly Detection in Text with Deviation Learning Anindya Sundar Das Aravind Ajay S. Saha M. Bhuyan 21 2 0 22 Aug 2023
Estimating Semantic Similarity between In-Domain and Out-of-Domain Samples Rhitabrat Pokharel Ameeta Agrawal OODD 27 2 0 01 Jun 2023
Multi-Level Knowledge Distillation for Out-of-Distribution Detection in Text Qianhui Wu Huiqiang Jiang Haonan Yin Börje F. Karlsson Chin-Yew Lin 35 10 0 21 Nov 2022
Unsolved Problems in ML Safety Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt 186 275 0 28 Sep 2021
Types of Out-of-Distribution Texts and How to Detect Them Udit Arora William Huang He He OODD 225 97 0 14 Sep 2021
Brittle Features May Help Anomaly Detection Kimberly T. Mai Toby P Davies Lewis D. Griffin 27 2 0 21 Apr 2021
Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detection Alexander Podolskiy Dmitry Lipin A. Bout Ekaterina Artemova Irina Piontkovskaya OODD 97 82 0 11 Jan 2021