Deep one-class classification via interpolated Gaussian descriptor
One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they of...
Saved in:
Main Authors: | CHEN, Yuanhong, TIAN, Yu, PANG, Guansong, CARNEIRO, Gustavo |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2022
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7034 https://ink.library.smu.edu.sg/context/sis_research/article/8037/viewcontent/19915_Article_Text_23928_1_2_20220628.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes
by: TIAN, Yu, et al.
Published: (2022) -
Learning transferable deep convolutional neural networks for the classification of bacterial virulence factors
by: ZHENG, Dandan, et al.
Published: (2020) -
Self-supervised multi-class pre-training for unsupervised anomaly detection and segmentation in medical images
by: TIAN, Yu, et al.
Published: (2021) -
Truncated Affinity Maximization: One-class homophily modeling for graph anomaly detection
by: QIAO, Hezhe, et al.
Published: (2023) -
Explainable deep few-shot anomaly detection with deviation networks
by: PANG, Guansong, et al.
Published: (2021)