WikiSysop: Created page with "== Citation == Gao, W., Wu, Y. and He, X. 2026. CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026), 28298–28307. == Abstract == Heterogeneous reconstruction in cryo-electron microscopy (Cryo-EM) is fundamental for understanding macromolecular structural diversity, yet remains challenging due to extreme noise, contin..."

2026-07-07T05:55:08Z

Created page with "== Citation == Gao, W., Wu, Y. and He, X. 2026. CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026), 28298–28307. == Abstract == Heterogeneous reconstruction in cryo-electron microscopy (Cryo-EM) is fundamental for understanding macromolecular structural diversity, yet remains challenging due to extreme noise, contin..."

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== Citation ==

Gao, W., Wu, Y. and He, X. 2026. CryoKRAQEN: Kernel-Regularized Annealing for Quantized Embedding Networks in Cryo-EM Heterogeneous Reconstruction. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2026), 28298–28307.

== Abstract ==

Heterogeneous reconstruction in cryo-electron microscopy
(Cryo-EM) is fundamental for understanding macromolecular
structural diversity, yet remains challenging due to
extreme noise, continuous conformational changes, and
ambiguous image-to-structure mappings. Existing neural
approaches often rely on encoder–decoder pipelines or
fixed codebooks, which can be computationally demanding
or struggle with complex heterogeneity. We propose
CryoKRAQEN, a decoder-only framework that integrates
triplane implicit representations with kernel-guided latent
assignment and quantized embeddings to improve stability
and structural discrimination. The method avoids encoder
dependencies and mitigates collapse during training,
enabling accurate modeling of both conformational and
compositional variations. Across diverse Cryo-EM benchmarks,
CryoKRAQEN delivers competitive performance,
robust reconstructions, and interpretable latent organization
compared to state-of-the-art neural and classical methods.
The project page is available at CryoKRAQEN.

== Keywords ==

== Links ==

https://openaccess.thecvf.com/content/CVPR2026/html/Gao_CryoKRAQEN_Kernel-Regularized_Annealing_for_Quantized_Embedding_Networks_in_Cryo-EM_Heterogeneous_CVPR_2026_paper.html

== Related software ==

== Related methods ==

== Comments ==

2026Gao CryoKRAQEN - Revision history