Reliable outlier detection is critical for real-world applications of deep learning models. Likelihoods produced by deep generative models, although extensively studied, have been largely dismissed as being impractical for outlier detection. For one, deep generative model likelihoods are readily biased by low-level input statistics. Second, many recent solutions for correcting these biases are computationally expensive or do not generalize well to complex, natural datasets. Here, we explore outlier detection with a state-of-the-art deep autoregressive model: PixelCNN++. We show that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies. We propose two families of bijective transformations that we term "shaking" and "stirring", which ameliorate low-level biases and isolate the contribution of long-range dependencies to the PixelCNN++ likelihood. These transformations are computationally inexpensive and readily applied at evaluation time. We evaluate our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed state-of-the-art outlier detection performance. In sum, lightweight remedies suffice to achieve robust outlier detection on images with deep generative models.

翻译：深基因模型所产生的可能性虽然经过广泛研究,但在很大程度上被否认为不切实际,无法进行异常检测。首先,深基因模型的可能性很容易受到低投入统计的偏差。第二,许多最近纠正这些偏差的解决方案在计算上代价高昂,或者没有被广泛归纳到复杂的自然数据集中。在这里,我们用一种最先进的深层自动递进模型(PixelCNN+++)来探索离异性检测。我们用五度灰度和六度自然图像数据集来广泛评估我们的方法,并显示它们达到或超过以当地依赖为基础的预测。我们建议两种双向转换的组合,即我们称为“摇动”和“振动”的“振动 ” 和“振动 ”,以降低低层次偏差,并将长期依赖性对像素CNN++可能性的贡献分离出来。这些转变在计算上成本低,在评估时很容易应用。我们用五度灰度和六度的自然图像数据集来评估我们的方法,并表明它们达到或超过状态的异常值,以稳健的基因探测模型。