Image reconstruction is likely the most predominant auxiliary task for image classification. In this paper, we investigate "approximating the Fourier Transform of the input image" as a potential alternative, in the hope that it may further boost the performances on the primary task or introduce novel constraints not well covered by image reconstruction. We experimented with five popular classification architectures on the CIFAR-10 dataset, and the empirical results indicated that our proposed auxiliary task generally improves the classification accuracy. More notably, the results showed that in certain cases our proposed auxiliary task may enhance the classifiers' resistance to adversarial attacks generated using the fast gradient sign method.

翻译：图像重建可能是图像分类的最主要辅助任务。 在本文中,我们调查了“ 采用输入图像的Fourier变换法” 作为一种潜在的替代方案,希望它能够进一步提升主要任务的绩效,或者引入图像重建没有很好覆盖的新的限制。 我们在CIFAR-10数据集上试验了五种流行的分类结构,实验结果表明,我们拟议的辅助任务一般会提高分类的准确性。 更值得注意的是,结果显示,在某些情况下,我们提议的辅助任务可能会加强分类者对使用快速梯度标志方法引发的对抗性攻击的抵抗力。