Impact of Noise Schedule Adaptation in ANT on Fidelity-Cost Trade-offs Across Monash Domains

Abstract

Abstract: Advances in diffusion models for generative artificial intelligence have recently propagated to the time series (TS) domain, demonstrating state-of-the-art performance on various tasks. However, prior works on TS diffusion models often borrow the framework of existing works proposed in other domains without considering the characteristics of TS data, leading to suboptimal performance. In this work, we propose Adaptive Noise schedule for Time series diffusion models (ANT), which automatically predetermines proper noise schedules for given TS datasets based on their statistics representing non-s

Research Question

What is the impact of varying the noise schedule adaptation mechanism in ANT on the trade-off between sample fidelity (measured by FID score) and computational cost (measured by GFLOPs) across different domains in Monash?

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