In the image-based detection of catenary anomalies, detection of bird's nest anomalies is a
typical situation. However, the image data containing the nests is only a small portion of total …

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 …

Many computer-vision based applications suffer from classification problems due to
imbalanced datasets. The applications like image-based disease analysis, accident …

One-class anomaly detection is conducted to identify anomalous instances with different
distributions from the expected normal instances. For this task, an Encoder-Decoder …

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 …

Convolutional neural network (CNN) models are widely used for image classification.
However, CNN models are vulnerable to out-of-distribution (OoD) samples. This …

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 …