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/JHLee0513/Salt_detection_challenge

Deep Residual Learning for Image Recognition

https://github.com/JL321/Proximal-Policy-Optimization

Proximal Policy Optimization Algorithms

https://github.com/JL321/mujo-2DHalf-Cheetah

Continuous control with deep reinforcement learning

https://github.com/JL321/mujo-2DWalker

Continuous control with deep reinforcement learning

https://github.com/JONGSKY/paper

Deep Residual Learning for Image Recognition

https://github.com/Jagannathrk2020/Face-detection-and-counting-

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Jagyan/ghost_radarpoint_detection

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

https://github.com/JaidedAI/EasyOCR

Deep Residual Learning for Image Recognition

https://github.com/James-Gilbert-/medical-deep-learning

Deep Residual Learning for Image Recognition

https://github.com/James-Gilbert-/pneumonia-detection

Deep Residual Learning for Image Recognition

https://github.com/JanFschr/DeepFaceLabTest

DeepFaceLab: Integrated, flexible and extensible face-swapping framework

https://github.com/JanMarcelKezmann/Residual-Network-Architectures

Deep Residual Learning for Image Recognition

https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/Jarr0d/Human-Parsing-Network

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/JaryHuang/awesome_SSD_FPN_GIoU

Focal Loss for Dense Object Detection

https://github.com/Javed002/Object_detection_PointNet

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

https://github.com/JayPatwardhan/ResNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/JeansonYang/VGG16-MindSpore

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Jeongyun-Lee-0423/Classification

Deep Residual Learning for Image Recognition

https://github.com/JesonZhang822/Dogs-VS-Cats-Project

Deep Residual Learning for Image Recognition

https://github.com/JeyesHan/P2LR

Deep Residual Learning for Image Recognition

https://github.com/Ji4chenLi/ARS_experiment

Simple random search provides a competitive approach to reinforcement learning

https://github.com/JiangShaoYin/DAN

Deep Alignment Network: A convolutional neural network for robust face alignment

https://github.com/Jingliang-Duan/Distributional-Soft-Actor-Critic

Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation Errors

https://github.com/Jo-dsa/SemanticSeg

Focal Loss for Dense Object Detection

https://github.com/JoeTracey/Portfolio

Deep Residual Learning for Image Recognition

https://github.com/JohannesAck/MATD3implementation

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/Johnny952/retinaface_mod

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/JonasGeiping/poisoning-gradient-matching

Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks

https://github.com/JonasRSV/DDPG

Continuous control with deep reinforcement learning

https://github.com/JonasRSV/DQNTensorflow

Parameter Space Noise for Exploration

https://github.com/JonasRSV/PGTensorflow

Continuous control with deep reinforcement learning

https://github.com/JonasRSV/PPO

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/Jopapy19/CNN-App

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/JorgeA-RD/Reinforcement-Learning-Agents/tree/main/DDPG

Continuous control with deep reinforcement learning

https://github.com/JoseLGP/FaceRecognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

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