  Image reconstruction is likely the most predominant auxiliary task for image
classification, but we would like to think twice about this convention. In this
paper, we investigated "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.
