Trusted Multi-View Learning Under Noisy Supervision

This paper addresses a problem in multi-view learning (training AI models using multiple types of data about the same object), where the training data contains incorrect labels that reduce model reliability in safety-critical applications. The authors propose TMNR and TMNR² methods that use evidential deep neural networks (models that estimate both predictions and confidence levels) to identify mislabeled data and learn effectively despite noisy supervision (imperfect training labels), achieving 7% accuracy improvements on heavily corrupted datasets.