Deep Learning
DifferentiableAugmentation forData-EfficientGANTraining
Big data has enabled deep learning algorithms achieve rapid advancements. In particular, stateof-the-art generative adversarial networks (GANs) [11] are able to generate high-fidelity natural images of diverse categories [2,18]. Many computer vision and graphics applications have been enabled[32,43,53].
A Unified Framework for Uniform Signal Recovery in Nonlinear Generative Compressed Sensing
Experimental results are presented to corroborate our theory.
E h dYθt +dbYφt |X0=x i, (24a) logρT(θ;x) LIPF(θ): =E[ Yθs + bYφs | Xs =x,s=0 ] = Z
SB-FBSDE isanewclass ofgenerativemodels that, inspiring bytherecent advance of understanding deep learning through the optimal control perspective [61-63], adopts Lemma 5 to generalize the score-based diffusion models.
