Diffusion Models for Unbalanced Multimodal Paired Scientific Data Generation
UB-Diff, a diffusion-based generative model that produces paired multi-modal scientific data (for example the spatial velocity map and the seismic waveform in subsurface imaging) even when the training data are unbalanced across modalities. Its key idea is a one-in-two-out encoder-decoder that maps available data into a shared co-latent representation guaranteeing the two output modalities stay paired, after which the diffusion process samples new pairs from that co-latent space; it is evaluated on the OpenFWI dataset and improves Frechet Inception Distance and pairwise-consistency over prior multi-modal generators.
A Novel Diffusion Model for Pairwise Geoscience Data Generation with Unbalanced Training Dataset
Recently, the advent of generative AI technologies has made transformational impacts on our daily lives, yet its application in scientific applications remains in its early stages. Data scarcity is a…