text: 456,152,1247369972948430848,288623330,Aaron Hertzmann,"Check out our new paper on discovering of controls for making images with GANs, without any supervision (other than optionally naming the controls afterward), by Erik Härkönen, with @jaakkolehtinen, @sylvain_paris, and myself. <LINK> <LINK> The basic idea is to use PCA to identify the important modes of variation that have been learned by a GAN. For such a simple algorithm, it’s surprisingly effective. Restricting controls to modify only a few network layers makes them even more useful. <LINK> Our methods give you a way to explore the types of variations that your GAN has learned. Pushing the sliders far past their normal ranges can make some intriguingly-unrealistic images. <LINK> We also show a very simple way to add StyleGAN-like style mixing to BigGAN. <LINK> The applications to owl customization are endless. <LINK> @c_dan4th I was, of course, thinking the exact same thing :)",https://arxiv.org/abs/2004.02546,"This paper describes a simple technique to analyze Generative Adversarial Networks (GANs) and create interpretable controls for image synthesis, such as change of viewpoint, aging, lighting, and time of day. We identify important latent directions based on Principal Components Analysis (PCA) applied either in latent space or feature space. Then, we show that a large number of interpretable controls can be defined by layer-wise perturbation along the principal directions. Moreover, we show that BigGAN can be controlled with layer-wise inputs in a StyleGAN-like manner. We show results on different GANs trained on various datasets, and demonstrate good qualitative matches to edit directions found through earlier supervised approaches. ",GANSpace: Discovering Interpretable GAN Controls,6,"['Check out our new paper on discovering of controls for making images with GANs, without any supervision (other than optionally naming the controls afterward), by Erik Härkönen, with @jaakkolehtinen, @sylvain_paris, and myself.\n<LINK> <LINK>', 'The basic idea is to use PCA to identify the important modes of variation that have been learned by a GAN. For such a simple algorithm, it’s surprisingly effective. Restricting controls to modify only a few network layers makes them even more useful. https://t.co/BgpgAcyPsj', 'Our methods give you a way to explore the types of variations that your GAN has learned. Pushing the sliders far past their normal ranges can make some intriguingly-unrealistic images. https://t.co/DL93xN7P38', 'We also show a very simple way to add StyleGAN-like style mixing to BigGAN. https://t.co/3JbBHKm14S', 'The applications to owl customization are endless. https://t.co/eACEg3kpLm', '@c_dan4th I was, of course, thinking the exact same thing :)']",20,04,892