Impact of Conditioning Mechanism Variation in Multimodal Time-Series Generative Models on Classifier Accuracy and Wasserstein

Abstract

Abstract: We present SR3, an approach to image Super-Resolution via Repeated Refinement. SR3 adapts denoising diffusion probabilistic models (Ho et al. 2020), (Sohl-Dickstein et al. 2015) to image-to-image translation, and performs super-resolution through a stochastic iterative denoising process. Output images are initialized with pure Gaussian noise and iteratively refined using a U-Net architecture that is trained on denoising at various noise levels, conditioned on a low-resolution input image. SR3 exhibits strong performance on super-resolution tasks at different magnification factors, on faces and

Research Question

How does conditioning mechanism variation in multimodal time-series generative models impact downstream classifier accuracy and Wasserstein distance?

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