Locally varying distance transform for unsupervised visual anomaly detection

Unsupervised anomaly detection on image data is notoriously unstable. We believe this is because many classical anomaly detectors implicitly assume data is low dimensional. However, image data is always high dimensional. Images can be projected to a low dimensional embedding but such projections rel...

Full description

Saved in:
Bibliographic Details
Main Authors: LIN, Wen-yan, LIU, Zhonghang, LIU, Siying
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2022
Subjects:
Online Access:https://ink.library.smu.edu.sg/sis_research/7310
https://ink.library.smu.edu.sg/context/sis_research/article/8313/viewcontent/1673.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English