SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network
This work addresses image translation problems for computer vision applications, but it is incremental as it builds on existing GAN and attention mechanisms.
The paper tackled unsupervised image translation for tasks like face style transform by introducing a GAN with spectral domain attention, achieving a significant performance improvement, such as reducing FID from 142.84 to 49.18.
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.