论文标题
部分可观测时空混沌系统的无模型预测
Out-of-Distribution Detection with Semantic Mismatch under Masking
论文作者
论文摘要
本文提出了一个新颖的分布（OOD）检测框架，称为图像分类器MoodCat。 MoodCat掩盖了输入图像的一个随机部分，并使用生成模型将蒙版图像合成为以分类结果为条件的新图像。然后，它计算原始图像与合成的综合图像之间的语义差。与现有解决方案相比，MoodCat自然会使用拟议的面具和条件合成策略来学习分布数据的语义信息，这对于识别OOD至关重要。实验结果表明，MoodCat的表现优于最先进的OOD检测解决方案。
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.
