One-class classification (OCC) algorithms aim to build classification models when the
negative class is either absent, poorly sampled or not well defined. This unique situation …

We have witnessed great interest and a wealth of promise in content-based image retrieval
as an emerging technology. While the last decade laid foundation to such promise, it also …

Recent image generation models such as Stable Diffusion have exhibited an impressive
ability to generate fairly realistic images starting from a simple text prompt. Could such …

Anomaly detection on attributed networks attracts considerable research interests due to
wide applications of attributed networks in modeling a wide range of complex systems …

Unsupervised representation learning has proved to be a critical component of anomaly
detection/localization in images. The challenges to learn such a representation are two-fold …

Deep autoencoder has been extensively used for anomaly detection. Training on the normal
data, the autoencoder is expected to produce higher reconstruction error for the abnormal …

Density-based and classification-based methods have ruled unsupervised anomaly
detection in recent years, while reconstruction-based methods are rarely mentioned for the …

Real-world timeseries have complex underlying temporal dynamics and the detection of
anomalies is challenging. In this paper, we propose the Temporal Hierarchical One-Class …

Reconstruction-based methods play an important role in unsupervised anomaly detection in
images. Ideally, we expect a perfect reconstruction for normal samples and poor …

Generative adversarial networks (GANs) are able to model the complex highdimensional
distributions of real-world data, which suggests they could be effective for anomaly …