Anomalous Example Detection in Deep Learning: A Survey
Deep Learning (DL) is vulnerable to out-of-distribution and adversarial examples resulting in incorrect outputs. To make DL more robust, several posthoc (or runtime) anomaly detection techniques to detect (and discard) these anomalous samples have been proposed in the recent past. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection for DL based applications. We provide a taxonomy for existing techniques based on their underlying assumptions and adopted approaches. We discuss various techniques in each of the categories and provide the relative strengths and weaknesses of the approaches. Our goal in this survey is to provide an easier yet better understanding of the techniques belonging to different categories in which research has been done on this topic. Finally, we highlight the unsolved research challenges while applying anomaly detection techniques in DL systems and present some high-impact future research directions.
Code (0)
등록된 구현이 없습니다.
Tasks
Anomaly DetectionDeep LearningSurveySimilar Papers 제목 키워드 기반
Time Series Anomaly Detection for Smart Grids: A Survey
With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges. One prominent challenge is to imple…
Anomaly DetectionSurveyTime SeriesTime Series Analysis+1Deep Learning for Time Series Anomaly Detection: A Survey
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The presence of anomalies can indicate novel or unexpected events, such as produ…
Anomaly DetectionDeep LearningSurveyTime Series+2A Survey on Social Media Anomaly Detection
Social media anomaly detection is of critical importance to prevent malicious activities such as bullying, terrorist attack planning, and fraud information dissemination. With the recent popularity of social media, new t…
Anomaly DetectionSurveyA Survey on GANs for Anomaly Detection
Anomaly detection is a significant problem faced in several research areas. Detecting and correctly classifying something unseen as anomalous is a challenging problem that has been tackled in many different manners over …
Anomaly DetectionSurveyVisual Analytics of Anomalous User Behaviors: A Survey
The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as ne…
Anomaly DetectionSurvey