Dataset · Hugging Face

sebasmos/latent-sr-embeddings

Name: latent-sr-embeddings
Creator: sebasmos
License: mit
Keywords: task_categories:image-to-image, language:en, license:mit, size_categories:10K<n<100K, format:text, modality:text, library:datasets, library:mlcroissant, arxiv:2604.12152, region:us, medical-imaging, super-resolution, embeddings, MRI, chest-x-ray

Latent-SR Embeddings: Precomputed VAE Latents for Medical Image Super-Resolution Precomputed VAE latent embeddings from the paper: "Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution"Sebastian Cajas, Ashaba Judith, Rahul Gorijavolu, Sahil Kapadia, Hillary Clinton Kasimbazi, Leo Kinyera, Emmanuel Paul Kwesiga, Sri Sri Jaithra Varma Manthena, Luis Filipe Nakayama, Ninsiima Doreen, Leo Anthony Celi.arXiv:2604.12152 (2026)… See the full description on the dataset page: https://huggingface.co/datasets/sebasmos/latent-sr-embeddings.

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Author / organization	sebasmos
License (as declared)	mit
Downloads	4.7K (as reported by Hugging Face)
Likes	1
Created	6 July 2026
Last updated at source	9 July 2026

medical-imagingsuper-resolutionembeddingsMRIchest-x-raylatent-diffusionVAE-embeddings

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Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-ResolutionPaper · cites

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Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-ResolutionPaper

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