Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders

In three linesVariational Autoencoder (C-VAE) trained on 1.5 million X-ray scattering images to learn low-dimensional representations. Model reveals organized clusters and generates controlled synthetic images. Deployed without retraining across two synchrotron facilities, outperforms DINOv3 in interpretability. Integrated into Latent Space Explorer (MLExchange).

Summary generated by Claude — human-verified