Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

3

코드

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

https://github.com/freddieee/pn_6d_single

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

https://github.com/fshamshirdar/pytorch-rdpg

Continuous control with deep reinforcement learning

https://github.com/ftdlyc/pointnet_pytorch

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

https://github.com/fulviomascara/pytorch-cv

Facial Expression Recognition Research Based on Deep Learning

https://github.com/fusion-ml/trajectory-information-rl

An Experimental Design Perspective on Model-Based Reinforcement Learning

https://github.com/fxia22/pointnet.pytorch

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

https://github.com/fznsakib/great-ape-behaviour-detector

Focal Loss for Dense Object Detection

https://github.com/gaetanserre/l2rpn-2022_ppo-baseline

Proximal Policy Optimization Algorithms

https://github.com/gaetanserre/lipo

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/gaobb/DLDL-v2

Learning Expectation of Label Distribution for Facial Age and Attractiveness Estimation

https://github.com/gary-kaitung/data-science-portfolio

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/garyzyr001/rethinking-airl

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/gatheluck/Stronghold

Deep Residual Learning for Image Recognition

https://github.com/gcastex/PruNet

A Light CNN for Deep Face Representation with Noisy Labels

https://github.com/gcr/torch-residual-networks

Deep Residual Learning for Image Recognition

https://github.com/genforce/fairgen

Improving the Fairness of Deep Generative Models without Retraining

https://github.com/genforce/ghfeat

Generative Hierarchical Features from Synthesizing Images

https://github.com/genforce/interfacegan

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/gengler1123/Triplet_Loss_Demo

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/georgesung/TD3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/georgkruse/cleanqrl

Continuous control with deep reinforcement learning

https://github.com/getcontrol/KYC-tensorflow

MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream

https://github.com/getcontrol/KYC-train-model

MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream

https://github.com/ghadahamed/darknet

Focal Loss for Dense Object Detection

https://github.com/ghaniskn/GCorp-Darknet

Focal Loss for Dense Object Detection

https://github.com/ghliu/pytorch-ddpg

Continuous control with deep reinforcement learning

https://github.com/ghostFaceKillah/expert

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

https://github.com/gijskoning/ReproducingCURL

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/ginn24/RefineDet-1

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/github-luffy/PFLD-68points-tensorflow

PFLD: A Practical Facial Landmark Detector

https://github.com/github-luffy/PFLD_68points_Pytorch

PFLD: A Practical Facial Landmark Detector

https://github.com/gitlimlab/Representation-Learning-by-Learning-to-Count

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/glarbi/Face-detection

Selective Refinement Network for High Performance Face Detection

https://github.com/glouppe/info8010-deep-learning

Deep Residual Learning for Image Recognition

https://github.com/gmlunesa/detectfacemask

Masked Face Recognition Dataset and Application

https://github.com/gmoss1/Kaggle-Halite-IV-RL

Proximal Policy Optimization Algorithms

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