  This work introduced a novel GAN architecture for unsupervised image
translation on the task of face style transform. A spectral attention-based
mechanism is embedded into the design along with spatial attention on the image
contents. We proved that neural network has the potential of learning complex
transformations such as Fourier transform, within considerable computational
cost. The model is trained and tested in comparison to the baseline model,
which only uses spatial attention. The performance improvement of our approach
is significant especially when the source and target domain include different
complexity (reduced FID to 49.18 from 142.84). In the translation process, a
spectra filling effect was introduced due to the implementation of FFT and
spectral attention. Another style transfer task and real-world object
translation are also studied in this paper.
