Zhang Lily H, Goldstein Mark, Ranganath Rajesh
New York University.
Proc Mach Learn Res. 2021 Jul;139:12427-12436.
Deep generative models (dgms) seem a natural fit for detecting out-of-distribution (ood) inputs, but such models have been shown to assign higher probabilities or densities to ood images than images from the training distribution. In this work, we explain why this behavior should be attributed to model misestimation. We first prove that no method can guarantee performance beyond random chance without assumptions on which out-distributions are relevant. We then interrogate the , the claim that relevant out-distributions can lie in high likelihood regions of the data distribution, and that ood detection should be defined based on the data distribution's typical set. We highlight the consequences implied by assuming support overlap between in- and out-distributions, as well as the arbitrariness of the typical set for ood detection. Our results suggest that estimation error is a more plausible explanation than the misalignment between likelihood-based ood detection and out-distributions of interest, and we illustrate how even minimal estimation error can lead to ood detection failures, yielding implications for future work in deep generative modeling and ood detection.
深度生成模型（DGMs）似乎很适合用于检测分布外（OOD）输入，但已证明此类模型对OOD图像分配的概率或密度高于来自训练分布的图像。在这项工作中，我们解释了为什么这种行为应归因于模型估计错误。我们首先证明，如果不对哪些分布外情况相关进行假设，就没有方法能够保证性能超过随机猜测。然后我们审视了这样一种说法，即相关的分布外情况可能位于数据分布的高似然区域，并且OOD检测应基于数据分布的典型集来定义。我们强调了假设内部分布和分布外情况之间存在支持重叠所带来的后果，以及用于OOD检测的典型集的任意性。我们的结果表明，估计误差比基于似然的OOD检测与感兴趣的分布外情况之间的不匹配更有可能是一种解释，并且我们说明了即使是最小的估计误差也可能导致OOD检测失败，这对深度生成建模和OOD检测的未来工作具有启示意义。
