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/xuzhiyuan1528/KTM-DRL

Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control

https://github.com/xyshadow/baseline_ddpg

Continuous control with deep reinforcement learning

https://github.com/xytpai/retinanet

Focal Loss for Dense Object Detection

https://github.com/y2kmz/pointnetv2

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

https://github.com/yahoo/open_nsfw

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/EfficientNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/faceboxes-pytorch

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

https://github.com/yakhyo/gaze-estimation

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/head-pose-estimation

Deep Residual Learning for Image Recognition

https://github.com/yakhyo/retinaface-pytorch

Deep Residual Learning for Image Recognition

https://github.com/yangboz/TransferLearning4Dentist

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/yangorwell/NGPlus

Deep Residual Learning for Image Recognition

https://github.com/yangyi02/densebox

DenseBox: Unifying Landmark Localization with End to End Object Detection

https://github.com/yangyucheng000/-insightface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/yangyucheng000/ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/yangyucheng000/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT

Feature Quantization Improves GAN Training

https://github.com/yangyucheng000/Paper-2/tree/main/ARCM-main

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC

Optimal Projections for Discriminative Dictionary Learning using the JL-lemma

https://github.com/yangyucheng000/Paper-4/tree/main/LADS-mindspore

LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization

https://github.com/yangyucheng000/Paper-4/tree/main/PDOC_mindspore

SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition

https://github.com/yangyucheng000/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/yangyucheng000/paper

CenterFace: Joint Face Detection and Alignment Using Face as Point

https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master

Optimal Projections for Discriminative Dictionary Learning using the JL-lemma

https://github.com/yangyucheng000/papercode-2/tree/main/LADS-mindspore

LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization

https://github.com/yangyucheng000/papercode-2/tree/main/SFAT-MindSpore-main

SFA: Small Faces Attention Face Detector

https://github.com/yangyucheng000/papercode-2/tree/main/TAA_MindSpore-main

TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation

https://github.com/yangyucheng000/pfld

PFLD: A Practical Facial Landmark Detector

https://github.com/yangyucheng000/pointnet

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

https://github.com/yangyucheng000/resnet_Ascend

Deep Residual Learning for Image Recognition

https://github.com/yangyucheng000/sphereface

SphereFace: Deep Hypersphere Embedding for Face Recognition

https://github.com/yannbouteiller/gym-airsimdroneracinglab

Real-Time Reinforcement Learning

https://github.com/yannbouteiller/rtgym

Reinforcement Learning with Random Delays

https://github.com/yanqi1811/attention-is-all-you-need

A Structured Self-attentive Sentence Embedding

https://github.com/yanx27/Pointnet

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

https://github.com/yanxp/PointNet

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

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