Deep neural networks (DNNs), due to their over-parameterization and memorization capacity, are prone to fitting incorrect labels in training data, which can substantially degrade their performance. Consequently, LNL has attracted increasing attention. Popular LNL methods select potentially clean samples for reliable su...
Crop diseases and pests severely threaten global food security. While deep learning has shown promise in controlled settings, real-world field conditions—characterized by complex backgrounds, variable lesion scales, and high inter-class similarity—remain challenging. To overcome limitations in feature representation, s...
Hong Zhang, Fa-Gen Song, Yong-Qi Yuan et al.· Entropy· 0 citations
Experimental results on CIFAR-10, CIFAR-100, Animal-10N, and Mini-WebVision, together with additional evaluation under open-set noise, show that the proposed CANNE method achieves competitive performance across diverse noisy-label settings.
Ge Jin, Qian Zhang, Li Huang et al.· Entropy· 0 citations
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