{"data":{"id":"23db2881-61bb-426d-8573-e6bd326bd8e0","title":"Trusted Multi-View Learning Under Noisy Supervision","summary":"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.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11506241","publishedAt":"2026-05-05T13:18:18.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"patchAvailable":null,"disclosureDate":"2026-05-05T13:18:18.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity"],"aiComponentTargeted":"training_data","llmSpecific":false,"classifierConfidence":0.85,"researchCategory":"peer_reviewed","atlasIds":null}}