most citedBrain Tumor Anomaly Detection via Latent Regularized Adversarial Network

9 citations · 19 across the 4 of their papers we have counts for

collaborators

5 papers

eess.IV20209 cited

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…

cs.CV20201 cited

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…

eess.AS20202 cited

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…

cs.CV20207 cited

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…

cs.CV2020

Anomaly Detection by One Class Latent Regularized Networks

Chengwei Chen, Pan Chen, Haichuan Song +4

Anomaly detection is a fundamental problem in computer vision area with many real-world applications. Given a wide range of images belonging to the normal class, emerging from some…