Title
Class-attribute inconsistency learning for novelty detection
Abstract
•A new notion of class-attribute inconsistency for novelty detection.•A novelty often has inconsistent class- and attribute-level similar references.•CAILNet outperforms state-of-the-arts by exploring and mining inconsistency.
Year
DOI
Venue
2022
10.1016/j.patcog.2022.108582
Pattern Recognition
Keywords
DocType
Volume
Novelty detection,Class-attribute inconsistency,Class-level similarity,Attribute-level similarity
Journal
126
ISSN
Citations 
PageRank 
0031-3203
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shuaiyuan Du101.69
Chaoyi Hong201.01
Yinpeng Chen300.34
Zhiguo Cao431444.17
Ziming Zhang500.34