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Rolling Shutter Correction

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하위 태스크: 0
평가표: 1
보존된 평가 기록: 8

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AnkurMali/ContinualPTNCN

공식 코드

Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations

https://github.com/Atcold/pytorch-PPUU

공식 코드

Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic

https://github.com/CNugteren/CLTune

공식 코드

CLTune: A Generic Auto-Tuner for OpenCL Kernels

https://github.com/GitCVfb/SUNet

공식 코드

SUNet: Symmetric Undistortion Network for Rolling Shutter Correction

https://github.com/GuoLanqing/ShadowDiffusion

공식 코드

ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal

https://github.com/HongtengXu/Relational-Factorization-Model

공식 코드

Gromov-Wasserstein Factorization Models for Graph Clustering

https://github.com/IRVLab/unrolling

공식 코드

IMU-Assisted Learning of Single-View Rolling Shutter Correction

https://github.com/JuanMarcosRamirez/LADMM_Net_Pytorch

공식 코드

LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data

https://github.com/KarelZhang/RUAS

공식 코드

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

https://github.com/Keziwen/Manifold_Net

공식 코드

Deep Manifold Learning for Dynamic MR Imaging

https://github.com/LiangZhangUMN/PSSE-via-DNNs

공식 코드

Real-time Power System State Estimation and Forecasting via Deep Neural Networks

https://github.com/Lukeli0425/Galaxy-Deconv

공식 코드

Galaxy Image Deconvolution for Weak Gravitational Lensing with Unrolled Plug-and-Play ADMM

https://github.com/Philip-Bachman/ICML-2015

공식 코드

Variational Generative Stochastic Networks with Collaborative Shaping

https://github.com/SOLLVE/clang

공식 코드

Design and Use of Loop-Transformation Pragmas

https://github.com/SimiaoZuo/Stackelberg-Adv

공식 코드

Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach

https://github.com/Subhadip-1/unrolling_meets_data_driven_regularization

공식 코드

End-to-end reconstruction meets data-driven regularization for inverse problems

https://github.com/VITA-Group/L2O-Training-Techniques

공식 코드

Training Stronger Baselines for Learning to Optimize

https://github.com/ZENGXH/DMM_Net

공식 코드

DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation

https://github.com/adwardlee/multitask-end-to-end-video-captioning

공식 코드

End-to-End Video Captioning with Multitask Reinforcement Learning

https://github.com/alanqrwang/HQSNet

공식 코드

Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data

https://github.com/arcananana/dssr

공식 코드

Learning Detail-Structure Alternative Optimization for Blind Super-Resolution

https://github.com/at3e/lwgnet

공식 코드

LWGNet: Learned Wirtinger Gradients for Fourier Ptychographic Phase Retrieval

https://github.com/baofff/BiSM

공식 코드

Bi-level Score Matching for Learning Energy-based Latent Variable Models

https://github.com/binjie-qin/msrpca-pbnet

공식 코드

Working memory inspired hierarchical video decomposition with transformative representations

https://github.com/biolins/insideness_data

공식 코드

Do Neural Networks for Segmentation Understand Insideness?

https://github.com/bmalezieux/unrolled_dl

공식 코드

Understanding approximate and unrolled dictionary learning for pattern recovery

https://github.com/btolooshams/stable-interpretable-unrolled-dl

공식 코드

Stable and Interpretable Unrolled Dictionary Learning

https://github.com/charliezcj/utvnet

공식 코드

Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement

https://github.com/cleverhans-lab/unrolling-sgd

공식 코드

Unrolling SGD: Understanding Factors Influencing Machine Unlearning

https://github.com/clyons19/CG-Net

공식 코드

A Compound Gaussian Least Squares Algorithm and Unrolled Network for Linear Inverse Problems

https://github.com/conlain-k/RLN_elasticity

공식 코드

Recurrent Localization Networks applied to the Lippmann-Schwinger Equation

https://github.com/cszcwu/gkmnet

공식 코드

Gaussian Kernel Mixture Network for Single Image Defocus Deblurring

https://github.com/cyyever/aaai_hydra

공식 코드

HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks

https://github.com/da-steve101/binary_connect_cifar

공식 코드

Unrolling Ternary Neural Networks

https://github.com/delinqu/qrsc

공식 코드

Towards Nonlinear-Motion-Aware and Occlusion-Robust Rolling Shutter Correction

https://github.com/fgfgama/unrolling-particles

공식 코드

Unrolling Particles: Unsupervised Learning of Sampling Distributions

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.