Improving out-of-distribution detection with disentangled foreground and background features

• Main Authors: DING, Choubo, PANG, Guansong
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Machine learning; Computer vision; Image representation; Anomaly detection; Out-of-Distribution detection; Disentangled representations; Artificial Intelligence and Robotics; Graphics and Human Computer Interfaces
• Online Access: https://ink.library.smu.edu.sg/sis_research/9756; https://ink.library.smu.edu.sg/context/sis_research/article/10756/viewcontent/2303.08727v2.pdf
• Institution: Singapore Management University

Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foregr...