PixOOD: Pixel-Level Out-of-Distribution Detection

We propose a pixel-level 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. The PixOOD consists of two main parts - in-distribution data model and decision strategy estimator. 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 (stochastic) gradient descent techniques. Furthermore, we propose two models for estimating decision strategy, per-class and unified calibration models, each suitable for different applications. 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.