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/DongDem/RangeLoss_MarginalLoss_Tensorflow

Range Loss for Deep Face Recognition with Long-tail

https://github.com/DongDem/RangeLoss_Marginal_Loss_Tensorflow

Range Loss for Deep Face Recognition with Long-tail

https://github.com/Dou-Yu-xuan/FSRNet

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

https://github.com/Dou-Yu-xuan/vggface2-pytorch

Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks

https://github.com/Doyosae/Deep-Complex-Networks

On Complex Valued Convolutional Neural Networks

https://github.com/Doyosae/Deep_Complex_Networks

On Complex Valued Convolutional Neural Networks

https://github.com/DrMMZ/RetinaNet

Focal Loss for Dense Object Detection

https://github.com/DrRyanHuang/VGGNet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Driesssens/ibp-pytorch

Learning model-based planning from scratch

https://github.com/Duplums/bhb10k-dl-benchmark

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/DylanWusee/pointconv_pytorch

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

https://github.com/ECE685-FinalProject/3D-object-recognition

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

https://github.com/EMsnap/RobotSorting

Focal Loss for Dense Object Detection

https://github.com/ENSTA-U2IS-AI/torch-uncertainty

Focal Loss for Dense Object Detection

https://github.com/Eclectic-Sheep/sheeprl

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/EconomistGrant/HTFE-tensortrade

Proximal Policy Optimization Algorithms

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/EdenMelaku/Transfer-Learning-Pytorch-Implmentation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/EiroaMD/final_project_IH_pneumonia

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ElenaRyumina/EMO-AffectNetModel

https://github.com/Eltomad/Noiseface

Noise-Tolerant Paradigm for Training Face Recognition CNNs

https://github.com/Eltomad/Tiny_Faces

Can we still avoid automatic face detection?

https://github.com/Eltomad/insightface

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/EndingCredits/gym

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

https://github.com/EngineeringSapien/NarutoCV

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/EricAlcaide/MiniFold

Deep Residual Learning for Image Recognition

https://github.com/EricZgw/PyramidBox

PyramidBox: A Context-assisted Single Shot Face Detector

https://github.com/EstherBear/implementation-of-pruning-filters

Deep Residual Learning for Image Recognition

https://github.com/EverLookNeverSee/diag2model

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/EverLookNeverSee/fer_tl_dcnn

https://github.com/EvgenyDyshlyuk/DeepLearning_face_detection_embeddings_clustering

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/EvgenyDyshlyuk/Deep_Learning_Face_Detection_Embeddings_Clustering

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/EvgenyDyshlyuk/Face_detection_embeddings_clustering

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/EvgenyVendrov/DeepLearningCourse

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ExplainableML/UncerGuidedI2I

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

https://github.com/ExplorerFreda/Structured-Self-Attentive-Sentence-Embedding

A Structured Self-attentive Sentence Embedding

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