Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Paper • 2610.22523 • Published
EMber: Learning the Shared Latent Structure of Cryo-EM Inverse Problems
Nithin Chalapathi · Theo Sternlieb · Ethan Han · Sonya M. Hanson · Pilar Cossio · Aditi S. Krishnapriyan · Ritwik Gupta
EMber is a family of pretrained vision transformers for extracting representations from single-particle cryo-EM particle images. Given a particle image, EMber produces an image-level embedding and spatial patch features for use in downstream analysis. The models were trained using self-distillation on over 50 million experimental particle images from 214 EMPIAR datasets, together with simulated projections from 268,000 molecular assemblies.
Check out our preprint here and code here.
EMber-Large has 304.2M parameters and uses a 16x16 patch size.
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