Matches in SemOpenAlex for { <https://semopenalex.org/work/W3135550350> ?p ?o ?g. }
- W3135550350 endingPage "38" @default.
- W3135550350 startingPage "1" @default.
- W3135550350 abstract "Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This paper surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in three high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages and disadvantages, and discuss how they address the aforementioned challenges. We further discuss a set of possible future opportunities and new perspectives on addressing the challenges." @default.
- W3135550350 created "2021-03-15" @default.
- W3135550350 creator A5000798681 @default.
- W3135550350 creator A5006294869 @default.
- W3135550350 creator A5028024287 @default.
- W3135550350 creator A5039104219 @default.
- W3135550350 date "2021-03-05" @default.
- W3135550350 modified "2023-10-13" @default.
- W3135550350 title "Deep Learning for Anomaly Detection" @default.
- W3135550350 cites W1242748811 @default.
- W3135550350 cites W1587559447 @default.
- W3135550350 cites W164607750 @default.
- W3135550350 cites W180931687 @default.
- W3135550350 cites W1876967670 @default.
- W3135550350 cites W1967456674 @default.
- W3135550350 cites W1970088130 @default.
- W3135550350 cites W1975900269 @default.
- W3135550350 cites W1978239142 @default.
- W3135550350 cites W1983836802 @default.
- W3135550350 cites W1991758224 @default.
- W3135550350 cites W1995443851 @default.
- W3135550350 cites W1999518899 @default.
- W3135550350 cites W2000412498 @default.
- W3135550350 cites W2010921848 @default.
- W3135550350 cites W2013619732 @default.
- W3135550350 cites W2015887370 @default.
- W3135550350 cites W2019014808 @default.
- W3135550350 cites W2026493302 @default.
- W3135550350 cites W2034365297 @default.
- W3135550350 cites W2045012776 @default.
- W3135550350 cites W2056081083 @default.
- W3135550350 cites W2061240327 @default.
- W3135550350 cites W2064029323 @default.
- W3135550350 cites W2089554624 @default.
- W3135550350 cites W2112796928 @default.
- W3135550350 cites W2117539524 @default.
- W3135550350 cites W2122361470 @default.
- W3135550350 cites W2122646361 @default.
- W3135550350 cites W2129281431 @default.
- W3135550350 cites W2132870739 @default.
- W3135550350 cites W2137130182 @default.
- W3135550350 cites W2145962650 @default.
- W3135550350 cites W2148583977 @default.
- W3135550350 cites W2163922914 @default.
- W3135550350 cites W2166128942 @default.
- W3135550350 cites W2169930371 @default.
- W3135550350 cites W2205836349 @default.
- W3135550350 cites W2282861635 @default.
- W3135550350 cites W2285233685 @default.
- W3135550350 cites W2296509296 @default.
- W3135550350 cites W2316630624 @default.
- W3135550350 cites W2340896621 @default.
- W3135550350 cites W2405933695 @default.
- W3135550350 cites W2621614835 @default.
- W3135550350 cites W2622370560 @default.
- W3135550350 cites W2740924709 @default.
- W3135550350 cites W2741406507 @default.
- W3135550350 cites W2765811365 @default.
- W3135550350 cites W2767094836 @default.
- W3135550350 cites W2769473018 @default.
- W3135550350 cites W2788154850 @default.
- W3135550350 cites W2807955733 @default.
- W3135550350 cites W2808771744 @default.
- W3135550350 cites W2809366716 @default.
- W3135550350 cites W2883725317 @default.
- W3135550350 cites W2914570111 @default.
- W3135550350 cites W2915683453 @default.
- W3135550350 cites W2925312408 @default.
- W3135550350 cites W2944250323 @default.
- W3135550350 cites W2948982773 @default.
- W3135550350 cites W2949848919 @default.
- W3135550350 cites W2953791858 @default.
- W3135550350 cites W2962736999 @default.
- W3135550350 cites W2962739339 @default.
- W3135550350 cites W2962791923 @default.
- W3135550350 cites W2962852342 @default.
- W3135550350 cites W2963058055 @default.
- W3135550350 cites W2963061824 @default.
- W3135550350 cites W2963307331 @default.
- W3135550350 cites W2963338867 @default.
- W3135550350 cites W2963523189 @default.
- W3135550350 cites W2963523627 @default.
- W3135550350 cites W2963610939 @default.
- W3135550350 cites W2963795951 @default.
- W3135550350 cites W2963899855 @default.
- W3135550350 cites W2972156365 @default.
- W3135550350 cites W2981650061 @default.
- W3135550350 cites W2996061341 @default.
- W3135550350 cites W3034292309 @default.
- W3135550350 cites W3034418897 @default.
- W3135550350 cites W3034942609 @default.
- W3135550350 cites W3035622304 @default.
- W3135550350 cites W3035699237 @default.
- W3135550350 cites W4239510810 @default.
- W3135550350 cites W4253461361 @default.
- W3135550350 cites W4254182148 @default.
- W3135550350 doi "https://doi.org/10.1145/3439950" @default.
- W3135550350 hasPublicationYear "2021" @default.