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Where is the Bottleneck of Adversarial Learning with Unlabeled Data?, , , , and . CoRR, (2019)Improving Medical Images Classification With Label Noise Using Dual-Uncertainty Estimation., , , , , , , and . IEEE Trans. Medical Imaging, 41 (6): 1533-1546 (2022)COVID-MTL: Multitask learning with Shift3D and random-weighted loss for COVID-19 diagnosis and severity assessment., , , , , , and . Pattern Recognit., (2022)LR-SVM+: Learning Using Privileged Information with Noisy Labels., , , , , , and . IEEE Trans. Multim., (2022)Towards Efficient Front-End Visual Sensing for Digital Retina: A Model-Centric Paradigm., , , , , , and . IEEE Trans. Multim., 22 (11): 3002-3013 (2020)How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation., , , , , , , , , and . CoRR, (2023)Multi-Class Classification from Noisy-Similarity-Labeled Data., , , , , , , and . CoRR, (2020)Parts-dependent Label Noise: Towards Instance-dependent Label Noise., , , , , , , , and . CoRR, (2020)FedFed: Feature Distillation against Data Heterogeneity in Federated Learning., , , , , , and . CoRR, (2023)Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels., , , , , , and . CoRR, (2020)