This paper proposes a novel out-of-distribution (OOD) detection framework named MoodCat for image classifiers. MoodCat masks a random portion of the input image and uses a generative model to synthesize the masked image to a new image conditioned on the classification result. It then calculates the semantic difference between the original image and the synthesized one for OOD detection. Compared to existing solutions, MoodCat naturally learns the semantic information of the in-distribution data with the proposed mask and conditional synthesis strategy, which is critical to identifying OODs. Experimental results demonstrate that MoodCat outperforms state-of-the-art OOD detection solutions by a large margin.

翻译：本文为图像分类者提出了一个名为 MoodCat 的新颖的分发外检测框架(OOD) 。 MoodCat 将输入图像的随机部分遮盖起来,并使用基因模型将遮盖的图像合成为以分类结果为条件的新图像。 然后计算原始图像与用于 OOD 检测的合成图像之间的语义差异。 与现有的解决方案相比, MoodCat 自然地学习了分配数据中的语义信息, 与拟议的遮罩和有条件合成战略相比, 这对于确定 OOODs 至关重要。 实验结果显示, MoodCat 大大超越了最先进的 OODD 检测解决方案 。