Feb. 28, 2024, 5:44 a.m. | Michael Kirchhof, Mark Collier, Seong Joon Oh, Enkelejda Kasneci

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.16569v2 Announce Type: replace-cross
Abstract: Accurate uncertainty estimation is vital to trustworthy machine learning, yet uncertainties typically have to be learned for each task anew. This work introduces the first pretrained uncertainty modules for vision models. Similar to standard pretraining this enables the zero-shot transfer of uncertainties learned on a large pretraining dataset to specialized downstream datasets. We enable our large-scale pretraining on ImageNet-21k by solving a gradient conflict in previous uncertainty modules and accelerating the training by up to …

arxiv cs.cv cs.lg type visual

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