Single Particle Analysis
25 papers with code • 0 benchmarks • 0 datasets
Single Particle Analysis
Benchmarks
These leaderboards are used to track progress in Single Particle Analysis
Most implemented papers
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
We introduce Habitat 2. 0 (H2. 0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios.
Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network
Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties.
Learning to recover orientations from projections in single-particle cryo-EM
Our approach consists of two steps: (i) the estimation of distances between pairs of projections, and (ii) the recovery of the orientation of each projection from these distances.
SpA-Former: Transformer image shadow detection and removal via spatial attention
In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image.
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.
DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM
Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM).
Classification via local manifold approximation
It is challenging to obtain accurate classification performance when the feature distributions in the different classes are complex, with nonlinear, overlapping and intersecting supports.
Improving the resolution of Cryo-EM single particle analysis
We proposed to enforce both sparsity and smoothness to improve the regularity of electron density map in the refinement process.
Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization
Momentum methods are now used pervasively within the machine learning community for training non-convex models such as deep neural networks.
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques.