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코드

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

https://github.com/rajatmodi62/PytorchWCT

Universal Style Transfer via Feature Transforms

https://github.com/rajatthepagal/DBPN-Keras

Deep Back-Projection Networks For Super-Resolution

https://github.com/rajbharat/PoseNet-V2V-Pytorch1.0-Win10

V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map

https://github.com/rami0205/ngramswin

SwinIR: Image Restoration Using Swin Transformer

https://github.com/ramonus/deep-style-transfer

A Neural Algorithm of Artistic Style

https://github.com/raunak222/Project_style_transfer-encryption-and-secure-sharing

A Neural Algorithm of Artistic Style

https://github.com/raunaks13/GeoNet-PyTorch

GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose

https://github.com/ravenwritingdesk/py-faster-rcnn-master

Mask R-CNN

https://github.com/ravindrabharathi/FaceAgingGAN

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/rayat137/Pose_3D

Exploiting temporal information for 3D pose estimation

https://github.com/raymon-tian/hourglass-facekeypoints-detection

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/raymondyeh07/chirality_nets

3D human pose estimation in video with temporal convolutions and semi-supervised training

https://github.com/rdutta1999/Patched-Face-Regeneration-v2

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/realmichaelye/NLP-State-of-the-art-Style-Transfer

Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer

https://github.com/rebnej/lick-caption-bias

Show and Tell: A Neural Image Caption Generator

https://github.com/redrock303/HEMlets

HEMlets PoSh: Learning Part-Centric Heatmap Triplets for 3D Human Pose and Shape Estimation

https://github.com/renmengqisheng/stereo_multifisheye

ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera Systems

https://github.com/renyang-home/ntire22_venh_sr

BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment

https://github.com/repyevsky/texture-nets

Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

https://github.com/revanurambareesh/multiperson

Unsupervised Cross-Modal Alignment for Multi-Person 3D Pose Estimation

https://github.com/revsic/torch-nansy

Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations

https://github.com/revsic/torch-retriever-vc

Retriever: Learning Content-Style Representation as a Token-Level Bipartite Graph

https://github.com/revygabor/WHENet

WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose

https://github.com/rf5/diffwave-unconditional

It's Raw! Audio Generation with State-Space Models

https://github.com/rf5/simple-sashimi

It's Raw! Audio Generation with State-Space Models

https://github.com/rflepp/efficient_mobile_denoising_models

Simple Baselines for Image Restoration

https://github.com/rg2/DeepFluoroLabeling-IPCAI2020

Pose Estimation of Periacetabular Osteotomy Fragments with Intraoperative X-Ray Navigation

https://github.com/rg321/anode_mtvso

Neural Ordinary Differential Equations

https://github.com/rh01/fast-rnn

Mask R-CNN

https://github.com/rh01/faster-rcnn

Mask R-CNN

https://github.com/rharish101/DeFMO

DeFMO: Deblurring and Shape Recovery of Fast Moving Objects

https://github.com/rhoposit/MOS_Estimation

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/rhoposit/MOS_Estimation2

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/rickyHong/GAN-TTS-repl2

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

https://github.com/rickyHong/GANTTS-update-repl

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

https://github.com/rickyHong/gantts-update-repl2

Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks

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