论文标题
使用分类器作为生成器
Use Classifier as Generator
论文作者
论文摘要
图像识别/分类是一个广泛研究的问题，但其相反的问题（图像产生）直到最近引起的关注程度要少得多。但是，最新的图像生成方法中的绝大多数方法都需要培训/重新培训分类器和/或具有某些约束的生成器，这可能很难实现。在本文中，我们提出了一种简单的方法，可以直接使用经过正常训练的分类器来生成图像。我们评估了我们的MNIST方法，并表明它可以通过实验质量有限的人眼产生可识别的结果。
Image recognition/classification is a widely studied problem, but its reverse problem, image generation, has drawn much less attention until recently. But the vast majority of current methods for image generation require training/retraining a classifier and/or a generator with certain constraints, which can be hard to achieve. In this paper, we propose a simple approach to directly use a normally trained classifier to generate images. We evaluate our method on MNIST and show that it produces recognizable results for human eyes with limited quality with experiments.
