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코드

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

https://github.com/Manchery/DeepPose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/MandyMo/pytorch_HMR

Stacked Hourglass Networks for Human Pose Estimation

https://github.com/ManifoldFR/recvis-project

RMPE: Regional Multi-person Pose Estimation

https://github.com/ManthanBhala/Style-Transfer-by-Rigid-Alignment

Style Transfer by Rigid Alignment in Neural Net Feature Space

https://github.com/MarkPrecursor/SRCNN-keras

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/Marshajennifer/Light-weight-Network-for-Human-Pose-Estimation

Attention-Enhanced Lightweight Hourglass Network for Human Pose Estimation

https://github.com/MarvinLavechin/imagetranslation-tensorflow

Instance Normalization: The Missing Ingredient for Fast Stylization

https://github.com/Mary-xl/HRnet_Kaggle_iNat2019_FGVC

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/Masao-Someki/CycleVAE_VC

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

https://github.com/MatteoT90/WibergianLearning

Rethinking Pose in 3D: Multi-stage Refinement and Recovery for Markerless Motion Capture

https://github.com/MatthewHowe/WIBAM

Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data

https://github.com/MattyChoi/PoseMachines

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/Maxencephilbert/In_Silico_Labeling_CBIO_internship

A Neural Algorithm of Artistic Style

https://github.com/MaximKuklin/3D_Object_Detection_Diploma

Objects as Points

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Unpaired_Image_to_Image_Translation_using_Cycle_Consistent_Adversarial_Networks

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

https://github.com/MayMiao0923/PESQ

MOSNet: Deep Learning based Objective Assessment for Voice Conversion

https://github.com/MayankGupta73/Workout-Evaluation-tf-openpose

Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields

https://github.com/Maymaher/StackGANv2

Generative Adversarial Text to Image Synthesis

https://github.com/Mckinsey666/Conditional-GAN-Anime-Generation

Generative Adversarial Text to Image Synthesis

https://github.com/MediaTek-NeuroPilot/mai21-learned-smartphone-isp

Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency

https://github.com/MegEngine/Models/tree/master/official/vision/keypoints

Simple Baselines for Human Pose Estimation and Tracking

https://github.com/Meghanshu-Bhatt/Neural-artistic-style-transfer

A Neural Algorithm of Artistic Style

https://github.com/MehmetBarutcu/PHM-Paper-Implementation

Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with $1/n$ Parameters

https://github.com/MenghaoGuo/Awesome-Vision-Attentions

Visual Attention Network

https://github.com/Mephisto405/Learning-Loss-for-Active-Learning

Learning Loss for Active Learning

https://github.com/MichaelKonobeev/neuralode-rl

Neural Ordinary Differential Equations

https://github.com/MicroprocessorX069/Pneumonia-detection-Dense-Conv-Net

CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

https://github.com/Microsoft/singleshotpose

BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth

https://github.com/MilesCranmer/lagrangian_nns

Neural Ordinary Differential Equations

https://github.com/Mind23-2/MindCode-101/tree/main/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Mind23-2/MindCode-101/tree/main/MIMO-UNet

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

https://github.com/Mind23-2/MindCode-101/tree/main/Non_local

Non-local Neural Networks

https://github.com/Mind23-2/MindCode-101/tree/main/lite-hrnet

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/Mind23-2/MindCode-118

Utilizing Self-supervised Representations for MOS Prediction

https://github.com/Mind23-2/MindCode-15

Objects as Points

https://github.com/Mind23-2/MindCode-166

Utilizing Self-supervised Representations for MOS Prediction

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