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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation., , , , , , , and . CoRR, (2022)Unsupervised label noise modeling and loss correction., , , , and . CoRR, (2019)Unsupervised Label Noise Modeling and Loss Correction., , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 312-321. PMLR, (2019)The travail involved in getting FDA approval . . . an overview on what it took to get FDA approval of a medical device with computer technology (A recent experience).. CBMS, page 28-29. IEEE, (1988)ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning., , , , and . IJCNN, page 1-8. IEEE, (2021)How Important is Importance Sampling for Deep Budgeted Training?, , , , and . BMVC, page 335. BMVA Press, (2021)Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning., , , , and . IJCNN, page 1-8. IEEE, (2020)ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning., , , , and . CoRR, (2020)Addressing out-of-distribution label noise in webly-labelled data., , , , and . CoRR, (2021)Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation., , , , , , and . CVPR Workshops, page 1635-1645. IEEE, (2022)