有限的作品显示无监督的分布（OOD）方法对复杂的医疗数据的功效。在这里，我们展示了我们无监督的OOD检测算法，SIMCLR-LOF的初步调查结果，以及在医学图像上应用的最近现实方法（SSD）的最新状态。SIMCLR-LOF使用SIMCLR学习语义有意义的功能，如果测试样本是ood的，则使用LOF进行评分。我们在多源国际皮肤成像协作（ISIC）2019数据集上进行了评估，并显示与SSD竞争的结果以及应用于同一数据的最近监督方法。

Reliable uncertainty quantification in deep neural networks is very crucial in safety-critical applications such as automated driving for trustworthy and informed decision-making. Assessing the quality of uncertainty estimates is challenging as ground truth for uncertainty estimates is not available. Ideally, in a well-calibrated model, uncertainty estimates should perfectly correlate with model error. We propose a novel error aligned uncertainty optimization method and introduce a trainable loss function to guide the models to yield good quality uncertainty estimates aligning with the model error. Our approach targets continuous structured prediction and regression tasks, and is evaluated on multiple datasets including a large-scale vehicle motion prediction task involving real-world distributional shifts. We demonstrate that our method improves average displacement error by 1.69% and 4.69%, and the uncertainty correlation with model error by 17.22% and 19.13% as quantified by Pearson correlation coefficient on two state-of-the-art baselines.