Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

2

보존된 평가 기록

3

코드

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

https://github.com/Gojay001/toolkit-DeepLearning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Gouet/Acrobot-PPO

Proximal Policy Optimization Algorithms

https://github.com/Gouet/Breakout-V0

Proximal Policy Optimization Algorithms

https://github.com/Gouet/DDPG_PendulumV1

Continuous control with deep reinforcement learning

https://github.com/Gouet/PPO-gym

Proximal Policy Optimization Algorithms

https://github.com/Gouet/PPO-pytorch

Proximal Policy Optimization Algorithms

https://github.com/Gowti-AiboT/GAN-based-SISR-Keras

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Graylab/deepH3-distances-orientations

Deep Residual Learning for Image Recognition

https://github.com/Gregory-Eales/proximal-policy-optimization

Proximal Policy Optimization Algorithms

https://github.com/Gryffindor112358/Arcface

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/Guillem96/efficient-net-tf

Focal Loss for Dense Object Detection

https://github.com/Guillem96/efficientdet-tf

Focal Loss for Dense Object Detection

https://github.com/GuoQuanhao/RetinaFace-Paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/GuoQuanhao/Tiny-Face-Paddle

Finding Tiny Faces

https://github.com/GuohongLi/face-alignment-pytorch

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

https://github.com/GurpartapSS/Realtime_FaceRecognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/HHTseng/video-classification

Deep Residual Learning for Image Recognition

https://github.com/HKUST-NISL/Multitask-Emotion-Recognition-with-Incomplete-Labels

Multitask Emotion Recognition with Incomplete Labels

https://github.com/HRNet/HRNet-FCOS

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-Facial-Landmark-Detection

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-Human-Pose-Estimation

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-Image-Classification

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-MaskRCNN-Benchmark

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-Object-Detection

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/HRNet/HRNet-Semantic-Segmentation

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/Halesu/4th-ML100Days

Deep Residual Learning for Image Recognition

https://github.com/HamadYA/GhostFaceNets

https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi

EmotionNet Nano: An Efficient Deep Convolutional Neural Network Design for Real-time Facial Expression Recognition

https://github.com/Hamiltonsjtu/based_on_facenet

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS

Talking Face Generation by Adversarially Disentangled Audio-Visual Representation

https://github.com/HannH/DMFN

Image Fine-grained Inpainting

https://github.com/HardikJ7321/AugmentedRandomSearch-AI-

Simple random search provides a competitive approach to reinforcement learning

https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Harry-Muzart/harry-muzart.github.io

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Harut0726/my-pointnet-tensorflow

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

https://github.com/HavenFeng/photometric_optimization

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