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

https://github.com/ColdenChan/GAT_D

Graph Attention Networks

https://github.com/Coldog2333/pytoflow

Video Enhancement with Task-Oriented Flow

https://github.com/CompVis/adaptive-style-transfer

A Style-Aware Content Loss for Real-time HD Style Transfer

https://github.com/CompVis/interactive-image2video-synthesis

Understanding Object Dynamics for Interactive Image-to-Video Synthesis

https://github.com/ContinualAI/avalanche

Expert Gate: Lifelong Learning with a Network of Experts

https://github.com/CorentinMAG/SRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Crazy-Jack/PredNet

Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning

https://github.com/CrazySherman/goodlife

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/CryptoSalamander/pytorch_paper_implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/CycloneBoy/PPDetectionPytorch

PP-YOLOE: An evolved version of YOLO

https://github.com/DASH-Lab/VFP290K

https://github.com/DELTA37/TVN

Tiny Video Networks

https://github.com/DJAlexJ/LRCN-for-Video-Regression

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

https://github.com/DLHacks/mocogan

MoCoGAN: Decomposing Motion and Content for Video Generation

https://github.com/DMarwan/NutricIA

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Dartum08/Video-Anomaly-Detection

Action Recognition with Dynamic Image Networks

https://github.com/Deci-AI/super-gradients

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Deeksha-Pandit/EVA4-S10

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/DeepMotionEditing/deep-motion-editing

Skeleton-Aware Networks for Deep Motion Retargeting

https://github.com/DeepakRaya/Cognitive_classification_RNN_EEG_images

Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks

https://github.com/Deepu1992/VideoClassification

Long-term Recurrent Convolutional Networks for Visual Recognition and Description

https://github.com/DegardinBruno/human_self_learning_anomaly

https://github.com/DengPingFan/DAVSOD

https://github.com/DengPingFan/Inf-Net

Video Polyp Segmentation: A Deep Learning Perspective

https://github.com/DepthAnything/Video-Depth-Anything

Video Depth Anything: Consistent Depth Estimation for Super-Long Videos

https://github.com/DerrickGXD/unsupervised-keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/DevashishJoshi/Transferring-GANs-FYP

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Diego999/pyGAT

Graph Attention Networks

https://github.com/Dir-b/PointNet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/DixinFan/st-gcn

Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition

https://github.com/Djmcflush/Funky-Movement

HP-GAN: Probabilistic 3D human motion prediction via GAN

https://github.com/DominikBatic/EndoViT

Whether and When does Endoscopy Domain Pretraining Make Sense?

https://github.com/DrRyanHuang/VGGNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/DuaneNielsen/keypoints

Unsupervised Learning of Object Keypoints for Perception and Control

https://github.com/Duplums/bhb10k-dl-benchmark

Very Deep Convolutional Networks for Large-Scale Image Recognition

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