Discriminative Graph-level Anomaly Detection via Dual-students-teacher Model

1 code implementation • 3 Aug 2023 • Fu Lin, Xuexiong Luo, Jia Wu, Jian Yang, Shan Xue, Zitong Wang, Haonan Gong

Then, two competing student models trained by normal and abnormal graphs respectively fit graph representations of the teacher model in terms of node-level and graph-level representation perspectives.

Anomaly Detection

Multi-representations Space Separation based Graph-level Anomaly-aware Detection

1 code implementation • 22 Jul 2023 • Fu Lin, Haonan Gong, Mingkang Li, Zitong Wang, Yue Zhang, Xuexiong Luo

The previous works have observed that abnormal graphs mainly show node-level and graph-level anomalies, but these methods equally treat two anomaly forms above in the evaluation of abnormal graphs, which is contrary to the fact that different types of abnormal graph data have different degrees in terms of node-level and graph-level anomalies.

Prompt-Learning for Cross-Lingual Relation Extraction

1 code implementation • 20 Apr 2023 • Chiaming Hsu, Changtong Zan, Liang Ding, Longyue Wang, Xiaoting Wang, Weifeng Liu, Fu Lin, Wenbin Hu

Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.

Relation Relation Extraction +1

Likelihood Landscapes: A Unifying Principle Behind Many Adversarial Defenses

no code implementations • 25 Aug 2020 • Fu Lin, Rohit Mittapalli, Prithvijit Chattopadhyay, Daniel Bolya, Judy Hoffman

Convolutional Neural Networks have been shown to be vulnerable to adversarial examples, which are known to locate in subspaces close to where normal data lies but are not naturally occurring and of low probability.

Adversarial Defense Adversarial Robustness