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The interest of the deep learning community in image synthesis has grownmassively in recent years. Nowadays, deep generative methods, and especiallyGenerative Adversarial Networks (GANs), are leading to state-of-the-artperformance, capable of synthesizing images that appear realistic. While theefforts for improving the quality of the generated images are extensive, mostattempts still consider the generator part as an uncorroborated "black-box". Inthis paper, we aim to provide a better understanding and design of the imagegeneration process. We interpret existing generators as implicitly relying onsparsity-inspired models. More specifically, we show that generators can beviewed as manifestations of the Convolutional Sparse Coding (CSC) and itsMulti-Layered version (ML-CSC) synthesis processes. We leverage thisobservation by explicitly enforcing a sparsifying regularization onappropriately chosen activation layers in the generator, and demonstrate thatthis leads to improved image synthesis. Furthermore, we show that the samerationale and benefits apply to generators serving inverse problems,demonstrated on the Deep Image Prior (DIP) method.

Improved Image Generation via Sparse Modeling