We propose to condition a generative model by a given image classifier uncertainty in order to analyze and explain its behavior. Preliminary experiments on synthetic data and a corrupted version of MNIST dataset illustrate the idea.

翻译：我们提出通过给定图像分类器的不确定性来条件化生成模型，以分析和解释其行为。在合成数据及MNIST数据集损坏版本上的初步实验验证了该方法的可行性。