Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

3

코드

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

https://github.com/Yihao-Sun/pytorch-mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/YijiongLin/ITER_KER_GER

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/Yijunmaverick/GenerativeFaceCompletion

Generative Face Completion

https://github.com/YiruS/pointnet_adversarial

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

https://github.com/Yizhao111/dreamer-pytorch

Learning Latent Dynamics for Planning from Pixels

https://github.com/YoUNG824/DDPG

Continuous control with deep reinforcement learning

https://github.com/Yonder-OSS/D3M-Primitives

Focal Loss for Dense Object Detection

https://github.com/YonghaoHe/LFFD-A-Light-and-Fast-Face-Detector-for-Edge-Devices

LFFD: A Light and Fast Face Detector for Edge Devices

https://github.com/Young98CN/pointconv_pytorch

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

https://github.com/YuanxunLu/LiveSpeechPortraits

Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation

https://github.com/YudeWang/UNet-Satellite-Image-Segmentation

Deep Residual Learning for Image Recognition

https://github.com/YuhangSong/Mega-Reward

Deep Reinforcement Learning that Matters

https://github.com/YuriCat/MuesliJupyterExample

Muesli: Combining Improvements in Policy Optimization

https://github.com/YutingZhang/lmdis-rep

Unsupervised Discovery of Object Landmarks as Structural Representations

https://github.com/Yuto0107/pointnet

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

https://github.com/YuvalNirkin/face_video_segment

On Face Segmentation, Face Swapping, and Face Perception

https://github.com/YvanG/VQGAN-CLIP

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/ZBigFish/FER-VT

https://github.com/ZHANG-SHI-CHANG/Pytorch_Retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ZTao-z/multiflow-resnet-ssd

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ZVK/Talking-Heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/ZVK/talking_heads

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/ZacharyGoshen/cifar-10-classifier

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Zachdr1/CNNs

Deep Residual Learning for Image Recognition

https://github.com/ZainRaza14/deepRL

Continuous control with deep reinforcement learning

https://github.com/Zartris/TD3_continuous_control

Proximal Policy Optimization Algorithms

https://github.com/Ze-Yang/Context-Transformer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Zhachory1/MusicNST

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ZhaoJ9014/face.evoLVe

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/ZhenJie-Zhang/darknet_shoe

Focal Loss for Dense Object Detection

https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow

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

https://github.com/ZhiwenShao/JAANet

Deep Adaptive Attention for Joint Facial Action Unit Detection and Face Alignment

https://github.com/ZiyangY/IndProject-RL-in-Supply-chain

Continuous control with deep reinforcement learning

https://github.com/ZmeiGorynych/transformer_pytorch

A Structured Self-attentive Sentence Embedding

https://github.com/ZoieMo/Multi-task

FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors

https://github.com/a-ozeki/openAI_gym

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

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