9 citations · 21 across the 7 of their papers we have counts for
7 papers
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection
Xiaofan Li, Zhizhong Zhang, Xin Tan +4
The vision-language model has brought great improvement to few-shot industrial anomaly detection, which usually needs to design of hundreds of prompts through prompt engineering. F…
Novelty Detection via Contrastive Learning with Negative Data Augmentation
Chengwei Chen, Yuan Xie, Shaohui Lin +5
Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous methods attempt to learn the representation of the…
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network
Nan Wang, Chengwei Chen, Yuan Xie +1
With the development of medical imaging technology, medical images have become an important basis for doctors to diagnose patients. The brain structure in the collected data is com…
Spoof Face Detection Via Semi-Supervised Adversarial Training
Chengwei Chen, Wang Yuan, Xuequan Lu +1
Face spoofing causes severe security threats in face recognition systems. Previous anti-spoofing works focused on supervised techniques, typically with either binary or auxiliary s…
Acoustic anomaly detection via latent regularized gaussian mixture generative adversarial networks
Chengwei Chen, Pan Chen, Lingyu Yang +4
Acoustic anomaly detection aims at distinguishing abnormal acoustic signals from the normal ones. It suffers from the class imbalance issue and the lacking in the abnormal instance…
Novelty Detection via Non-Adversarial Generative Network
Chengwei Chen, Wang Yuan, Yuan Xie +4
One-class novelty detection is the process of determining if a query example differs from the training examples (the target class). Most of previous strategies attempt to learn the…