Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

One-Class Classification (OCC) is a special case of multi-class classification, where data
observed during training is from a single positive class. The goal of OCC is to learn a …

In recent years, driven by the continuous development of mobile Internet technology and
artificial intelligence technology, the improvement of the manufacturing level of 6G Internet …

Deep anomaly detection, which utilizes neural networks to discover anomalies, is a vital
research topic in pattern recognition. With the burgeoning of inference mechanism …

Recently, the autoencoder (AE)-based methods in hyperspectral anomaly detection (HAD)
have attracted a lot of attention from scholars and researchers, and they acquire satisfying …

Deep autoencoder‐based methods are the majority of deep anomaly detection. An
autoencoder learning on training data is assumed to produce higher reconstruction error for …

Anomaly detection is a widely explored domain in machine learning. Many models are
proposed in the literature, and compared through different metrics measured on various …

Efficient defect detection in solar cell manufacturing is crucial for stable green energy
technology manufacturing. This paper presents a deep-learning-based automatic detection …

This paper summarizes the music demixing (MDX) track of the Sound Demixing Challenge
(SDX'23). We provide a summary of the challenge setup and introduce the task of robust …

Autoencoder based methods are the majority of deep unsupervised outlier detection
methods. However, these methods perform not well on complex image datasets and suffer …