Noise Type Classification

Noise type classification focuses on identifying and categorizing different types of noise in various data modalities, aiming to improve the accuracy and efficiency of downstream tasks like anomaly detection and signal processing. Current research employs diverse approaches, including deep learning models such as convolutional neural networks (CNNs), variational autoencoders (VAEs), and diffusion probabilistic models, often integrated with techniques like contrastive training or variational inference for enhanced performance. This field is crucial for advancing numerous applications, from improving the reliability of software systems and enhancing image/speech processing to optimizing the performance of scientific instruments like neutrino detectors.