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2 accepted papers

2024

Trusted Deep Domain Adaptation with Uncertainty Measure Based on Evidence Theory

ICASSP 2024accepted

Domain adaptation aims to build up an adaptive model for the learning tasks on target domain by using the data from source domain. Existing domain adaptation methods focused on reducing the gap between the data distributions of source domain and target domain but neglected the uncertainty of source…

Cited by 0SourceScholar
2023

Trusted Fine-Grained Image Classification through Hierarchical Evidence Fusion

AAAI 2023technical

Fine-Grained Image Classification (FGIC) aims to classify images into specific subordinate classes of a superclass. Due to insufficient training data and confusing data samples, FGIC may produce uncertain classification results that are untrusted for data applications. In fact, FGIC can be viewed as…

Cited by 9SourcePDFScholar