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Edited by
Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST),Yonina C. Eldar, Weizmann Institute of Science, Israel,Michael Unser, École Polytechnique Fédérale de Lausanne
CryoGAN uses ideas from deep generative adversarial learning to perform image reconstruction in single-particle cryo-electron microscopy (cryo-EM). In this chapter, we begin by introducing single-particle cryo-EM. We then formulate the associated image-reconstruction problem and discuss the main solutions found in the literature. Next, we describe the CryoGAN algorithm and show some representative results. Finally, we discuss what our experiences with Cryo-GAN suggest about the advantages and disadvantages of such deep generative adversarial methods in single-particle cryo-EM and beyond.
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