Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
Researchers have developed a domain-specific Convolutional Variational Autoencoder (C-VAE) to process large-scale X-ray scattering data, which is generated faster than traditional methods can handle. This model, trained on 1.5 million images, creates low-dimensional representations that organize structural variations and support synthetic data generation. When applied to real-time experiments, the C-VAE effectively structures complex processes into interpretable latent spaces, outperforming general-purpose models like DINOv3 (ViT-7B) in organizing scientific data. AI