3 papers
cs.LG2025
Generative Latent Diffusion for Efficient Spatiotemporal Data Reduction
Xiao Li, Liangji Zhu, Anand Rangarajan +1
Generative models have demonstrated strong performance in conditional settings and can be viewed as a form of data compression, where the condition serves as a compact representati…
cs.LG2025
Guaranteed Conditional Diffusion: 3D Block-based Models for Scientific Data Compression
Jaemoon Lee, Xiao Li, Liangji Zhu +2
This paper proposes a new compression paradigm -- Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) -- for lossy scientific data compression. The framework is based o…
cs.LG2024
Foundation Model for Lossy Compression of Spatiotemporal Scientific Data
Xiao Li, Jaemoon Lee, Anand Rangarajan +1
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module.…