Deep Coded Aperture Design: An End-To-End Approach For Computational Imaging Tasks

in this work, we propose a new parameter efficient sharing method for the training of GAN generators. While there has been recent progress in transfer learning for generative models with limited data, they are either limited to domains close to the original one, or adapt a large part of the parameters. This is somewhat redundant, as the goal of transfer learning should be to re-use old features. in this way, we propose width wise parameter sharing, which can learn a new domain with ten times fewer trainable parameters without a significant drop in quality. Previous approaches are less flexible than our method and also fail to preserve image quality for challenging transfers. Finally, as our goal is ultimately parameter re-use, we show that our method performs well in the multi-domain setting, wherein several domains are learned simultaneously with higher visual quality than the state of the art StarGAN-V2.