We propose a dense image prediction out-of-distribution detection algorithm, called PixOOD, which does not require training on samples of anomalous data and is not designed for a specific application which avoids traditional training biases. In order to model the complex intra-class variability of the in-distribution data at the pixel level, we propose an online data condensation algorithm which is more robust than standard K-means and is easily trainable through SGD. We evaluate PixOOD on a wide range of problems. It achieved state-of-the-art results on four out of seven datasets, while being competitive on the rest. The source code is available at https://github.com/vojirt/PixOOD.

翻译：我们提出了一种称为PixOOD的密集图像预测分布外检测算法，该算法无需在异常数据样本上进行训练，且并非针对特定应用设计，从而避免了传统训练偏差。为了在像素级别建模分布内数据复杂的类内变异性，我们提出了一种在线数据凝聚算法，该算法比标准K-means更具鲁棒性，且易于通过随机梯度下降进行训练。我们在多种问题上评估了PixOOD，其在七个数据集中的四个上取得了最先进的结果，并在其余数据集上保持竞争力。源代码发布于https://github.com/vojirt/PixOOD。