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Found 2 papers, 1 papers with code
Regularized Training of Intermediate Layers for Generative Models for Inverse Problems
For both of these inversion algorithms, we introduce a new regularized GAN training algorithm and demonstrate that the learned generative model results in lower reconstruction errors across a wide range of under sampling ratios when solving compressed sensing, inpainting, and super-resolution problems.
State Space Representations of Deep Neural Networks
This paper deals with neural networks as dynamical systems governed by differential or difference equations.
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