Improving the reliability of deployed machine learning systems often involves
developing methods to detect out-of-distribution (OOD) inputs. However,
existing research often narrowly focuses on samples from classes that are
absent from the training set, neglecting other types of plausible distribution
shifts. This limitation reduces the applicability of these methods in
real-world scenarios, where systems encounter a wide variety of anomalous
inputs. In this study, we categorize five distinct types of distribution shifts
and critically evaluate the performance of recent OOD detection methods on each
of them. We publicly release our benchmark under the name BROAD (Benchmarking
Resilience Over Anomaly Diversity). Our findings reveal that while these
methods excel in detecting unknown classes, their performance is inconsistent
when encountering other types of distribution shifts. In other words, they only
reliably detect unexpected inputs that they have been specifically designed to
expect. As a first step toward broad OOD detection, we learn a generative model
of existing detection scores with a Gaussian mixture. By doing so, we present
an ensemble approach that offers a more consistent and comprehensive solution
for broad OOD detection, demonstrating superior performance compared to
existing methods. Our code to download BROAD and reproduce our experiments is
publicly available.

我们在广泛的 OOD 检测中发现现有方法的性能不一致，因此我们提出了一种基于高斯混合模型的集成方法，从而提供了一种更一致和全面的解决方案，相比现有方法表现出更优异的性能。