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 this https URL

我们提出了一种名为 PixOOD 的密集图像预测的离群检测算法，它不需要对异常数据样本进行训练，也不针对特定应用程序，避免了传统训练偏差。我们提出了一种在线数据压缩算法，用于在像素级别建模类内数据的复杂内部类变异性，该算法比标准 K-means 更强大，通过随机梯度下降（SGD）更容易训练。我们在多个问题上对 PixOOD 进行了评估，它在七个数据集中的四个上取得了最先进的结果，并在其余数据集上具有竞争力。源代码可在此 URL 处获得