X Han, X Chen, LP Liu - Proceedings of the AAAI Conference on …, 2021 - ojs.aaai.org
When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works modify Generative Adversarial …
Deep learning is used in various application, and there are many outstanding performances in many fields recently. Generative Adversarial Networks (GAN) is one of the deep learning …
D Pattanayak, K Patel - 2022 International Conference for …, 2022 - ieeexplore.ieee.org
Many computer-vision based applications suffer from classification problems due to imbalanced datasets. The applications like image-based disease analysis, accident …
Y Dong, Z Zhang, H Peng… - Journal of Imaging …, 2023 - search.ebscohost.com
One-class anomaly detection is conducted to identify anomalous instances with different distributions from the expected normal instances. For this task, an Encoder-Decoder …
KW Huang, GW Chen, ZH Huang, SH Lee - Applied Sciences, 2023 - mdpi.com
Anomaly detection is an important research topic in the field of artificial intelligence and visual scene understanding. The most significant challenge in real-world anomaly detection …
D Lehmann, M Ebner - Artificial Neural Networks and Machine Learning …, 2021 - Springer
Convolutional neural network (CNN) models are widely used for image classification. However, CNN models are vulnerable to out-of-distribution (OoD) samples. This …
J Lee, H Kim, Y Hong… - Advances in Neural …, 2021 - proceedings.neurips.cc
Despite remarkable performance in producing realistic samples, Generative Adversarial Networks (GANs) often produce low-quality samples near low-density regions of the data …
Abstract Generative Adversarial Networks (GANs) are commonly used as a system able to perform unsupervised learning. We propose and demonstrate the use of a GAN architecture …