activity
20182022
most citedExplainable Deep Few-shot Anomaly Detection with Deviation Networks

43 citations · 80 across the 8 of their papers we have counts for

collaborators

15 papers

cs.LG20221 cited

Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment

Qizhou Wang, Guansong Pang, Mahsa Salehi +2

Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled…

cs.CV202211 cited

Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection

Choubo Ding, Guansong Pang, Chunhua Shen

Despite most existing anomaly detection studies assume the availability of normal training samples only, a few labeled anomaly examples are often available in many real-world appli…

cs.CV2022

Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame Detection

Yu Tian, Guansong Pang, Fengbei Liu +5

Current polyp detection methods from colonoscopy videos use exclusively normal (i.e., healthy) training images, which i) ignore the importance of temporal information in consecutiv…

cs.CV202143 cited

Explainable Deep Few-shot Anomaly Detection with Deviation Networks

Guansong Pang, Choubo Ding, Chunhua Shen +1

Existing anomaly detection paradigms overwhelmingly focus on training detection models using exclusively normal data or unlabeled data (mostly normal samples). One notorious issue…

cs.LG2021

DRAM Failure Prediction in AIOps: Empirical Evaluation, Challenges and Opportunities

Zhiyue Wu, Hongzuo Xu, Guansong Pang +4

DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers. However, limited work has been d…

cs.LG20211 cited

Homophily Outlier Detection in Non-IID Categorical Data

Guansong Pang, Longbing Cao, Ling Chen

Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Indepen…