The image inverse problem is the problem of reconstructing an image given its degraded or compressed observation. Some previous solutions to this problem use generative adversarial networks (GANs), but the representation capabilities of such models cannot capture the full distribution of complex classes of images (e.g., human faces), thus producing sub-optimal results. Our work examines the image-adaptive generative model, proposed in Hussein et al (2020), that purports to mitigate the limited representation capabilities of previous models in solving the image inverse problem. To this end, we implement the proposed model from Hussein et al (2020), which makes generators "image-adaptive" to a specific test sample. This model consists of three successive optimization stages: the non-image-adaptive "compressed sensing using generative models" (CSGM), the image-adaptive step (IA), and the post-processing "back-projection" (BP). Our results demonstrate that the two image-adaptive approaches--IA and BP--can effectively improve reconstructions. Further testing reveals slight biases existing in the model (e.g., skin tones), which we conjecture to be caused by the training dataset on which the model is trained. Finally, to explore more efficient ways of running the model, we test out different numbers of iterations used for CSGM. The results show that we can indeed decrease the number of CSGM iterations without compromising reconstruction qualities.
Antonio Marino ('23), Yilong Song ('23), and Daisuke Yamada ('23), under supervision of Professor Anna Rafferty. Carleton College.