“On Self Modulation for Generative Adversarial Networks”, 2018-10-02 (; backlinks; similar):
Training Generative Adversarial Networks (GANs) is notoriously challenging.
We propose and study an architectural modification, self-modulation, which improves GAN performance across different data sets, architectures, losses, regularizers, and hyperparameter settings. Intuitively, self-modulation allows the intermediate feature maps of a generator to change as a function of the input noise vector. While reminiscent of other conditioning techniques, it requires no labeled data.
In a large-scale empirical study we observe a relative decrease of 5%–35% in FID. Furthermore, all else being equal, adding this modification to the generator leads to improved performance in 124⁄144 (86%) of the studied settings.
Self-modulation is a simple architectural change that requires no additional parameter tuning, which suggests that it can be applied readily to any GAN.