One Class

One-class learning focuses on building models that can identify data points belonging to a single class, without requiring examples of other classes. Current research emphasizes improving the robustness and generalization of these models across diverse applications, employing techniques like multi-tasking, knowledge distillation, and novel loss functions within architectures such as autoencoders and neural networks trained with methods like the forward-forward algorithm. This approach is proving valuable in various fields, including anomaly detection in medical imaging and speech recognition, recommendation systems, and environmental monitoring, where obtaining comprehensive labeled datasets is challenging or impossible. The development of more effective one-class models holds significant potential for improving the accuracy and efficiency of various machine learning applications.