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Intermediate Layer Optimization for Inverse Problems using Deep Generative Models
Giannis Daras · Joseph Dean · Ajil Jalal · Alexandros Dimakis

Wed Jul 21 07:45 AM -- 07:50 AM (PDT) @
in Sparsity and Compressed Sensing »

We propose Intermediate Layer Optimization (ILO), a novel optimization algorithm for solving inverse problems with deep generative models. Instead of optimizing only over the initial latent code, we progressively change the input layer obtaining successively more expressive generators. To explore the higher dimensional spaces, our method searches for latent codes that lie within a small l1 ball around the manifold induced by the previous layer. Our theoretical analysis shows that by keeping the radius of the ball relatively small, we can improve the established error bound for compressed sensing with deep generative models. We empirically show that our approach outperforms state-of-the-art methods introduced in StyleGAN2 and PULSE for a wide range of inverse problems including inpainting, denoising, super-resolution and compressed sensing.

Keywords: Sparsity and Compressed Sensing · Algorithms

Author Information

Giannis Daras (The University of Texas at Austin)
Joseph Dean (UT Austin)
Ajil Jalal (University of Texas at Austin)
Alexandros Dimakis (UT Austin)

Alex Dimakis is an Associate Professor at the Electrical and Computer Engineering department, University of Texas at Austin. He received his Ph.D. in electrical engineering and computer sciences from UC Berkeley. He received an ARO young investigator award in 2014, the NSF Career award in 2011, a Google faculty research award in 2012 and the Eli Jury dissertation award in 2008. He is the co-recipient of several best paper awards including the joint Information Theory and Communications Society Best Paper Award in 2012. His research interests include information theory, coding theory and machine learning.

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