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
180x. We find that the pretrained uncertainties generalize to unseen datasets.
In scrutinizing the learned uncertainties, we find that they capture aleatoric
uncertainty, disentangled from epistemic components. We demonstrate that this
enables safe retrieval and uncertainty-aware dataset visualization. To
encourage applications to further problems and domains, we release all
pretrained checkpoints and code under this https URL .

准确的不确定性估计对可信赖的机器学习至关重要，本研究引入了视觉模型的首个预训练不确定性模块，类似于标准的预训练，这使得在大型预训练数据集上学习到的不确定性能够零样本迁移到专门的下游数据集，我们通过解决以前的不确定性模块中的梯度冲突并将训练加速了 180 倍，实现了在 ImageNet-21k 上的大规模预训练，我们发现预训练的不确定性能够泛化到未见过的数据集，通过审视学习到的不确定性，我们发现它们捕捉了与认知成分独立的基本不确定性，我们证明了这使得安全获取和不确定性感知的数据集可视化成为可能，为了鼓励将这项研究应用于更多的问题和领域，我们在以下链接中发布了所有预训练的检查点和代码。