Semi Supervised Anomaly Detection

Semi-supervised anomaly detection aims to improve anomaly detection accuracy by leveraging a small set of labeled anomalies alongside a larger volume of unlabeled data. Current research focuses on developing robust models that handle contaminated unlabeled data and address class imbalance, employing techniques like autoencoders, generative adversarial networks (GANs), support vector machines (SVMs) with quantum kernels, and reinforcement learning. These advancements are significant for various applications, including medical image analysis, industrial process monitoring, and cybersecurity, where labeled anomaly data is scarce but crucial for effective detection.