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computer-vision-7

Single Particle Analysis

3260 papers • 126 benchmarks • 313 datasets

(Image credit: Papersgraph)

Benchmarks

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Libraries

Use these libraries to find single-particle-analysis-7 models and implementations

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Subtasks

2D Particle Picking

Most implemented papers

Habitat 2.0: Training Home Assistants to Rearrange their Habitat

Z. Kira, Dhruv Batra, V. Koltun, Jitendra Malik, Angel X. Chang, M. Savva, Oleksandr Maksymets, Yili Zhao, Aaron Gokaslan, Erik Wijmans, Alexander Clegg, John Turner, Eric Undersander, Wojciech Galuba, Andrew Szot, Mustafa Mukadam, Devendra Singh Chaplot, Vladimír Vondruš, Noah Maestre, Sameer Dharur, Franziska Meier•Sun Jun 27 2021

Habitat 2.0 is introduced, a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios, and it is found that flat RL policies struggle on HAB compared to hierarchical ones, and SPA pipelines are more brittle than RL policies.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

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Cloud Removal for Remote Sensing Imagery via Spatial Attention Generative Adversarial Network

Heng Pan•Sun Sep 27 2020

Generative adversarial network is adopted to solve the remote sensing imagery cloud removal task and the spatial attention mechanism is introduced, which imitates the human visual mechanism and recognizes and focuses the cloud area with local-to-global spatial attention, thereby enhancing the information recovery of these areas and generating cloudless images with better quality.

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Learning to recover orientations from projections in single-particle cryo-EM

Michaël Defferrard, J. Banjac, Laurène Donati•Mon Apr 12 2021

The proposed method offers a novel learning-based take on orientation recovery in SPA, and demonstrates that orientations can be accurately recovered from projections that are shifted and corrupted with a high level of noise.

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SpA-Former: Transformer image shadow detection and removal via spatial attention

Xiao Feng Zhang, Chaonan Gu, Shangdong Zhu•Tue Jun 21 2022

An end-to-end SpA-Former to recover a shadow-free image from a single shaded image, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection.

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DeepPicker: a Deep Learning Approach for Fully Automated Particle Picking in Cryo-EM

Feng Wang, Huichao Gong, Gaochao liu, Meijing Li, Chuangye Yan, Tian Xia, Xueming Li, Jianyang Zeng•Thu May 05 2016

Tests on the recently-published cryo-EM data of three complexes have demonstrated that the deep learning based scheme, DeepPicker, can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts.

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Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration

Kui Jiang, Gang Wu, Junjun Jiang, Xianming Liu•Mon Sep 11 2023

A novel contrastive learning-based image restoration method by ’learning from history’, which dynamically generates negative samples from the historical models, which rejuvenates historical models as negative models, making it compatible with diverse image restoration tasks and without additional priors of the tasks.

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Classification via local manifold approximation

Didong Li, D. Dunson•Sat Mar 02 2019

This article proposes a new type of classifier based on obtaining a local approximation to the support of the data within each class in a neighborhood of the feature to be classified, and assigning thefeature to the class having the closest support.

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Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

D. Whiteson, P. Baldi, A. Shmakov, S. Hsu, M. Fenton, Ta-Wei Ho•Sun Oct 18 2020

This work trains one such network to identify and assign the decay products of each top quark unambiguously and without combinatorial explosion, and significantly outperforms existing state-of-the-art methods.

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Momentum via Primal Averaging: Theoretical Insights and Learning Rate Schedules for Non-Convex Optimization

Aaron Defazio•Wed Sep 30 2020

A Lyapunov analysis of SGD with momentum (SGD+M), by utilizing a equivalent rewriting of the method known as the stochastic primal averaging (SPA) form, which is much tighter than previous theory in the non-convex case.

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Single Particle Analysis | State-of-the-Art