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/Krisztina-Sinkovics/IDcard-visibility-classifier

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

https://github.com/KuanLin999/Occlusion-Robust-Face-Recognition-Based-on-Mask-Learning-with-Pairwise-Differential-Siamese-Network

Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network

https://github.com/KumapowerLIU/DeFLOCNet

SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color

https://github.com/LIU1514/Yolov3-

Focal Loss for Dense Object Detection

https://github.com/LKLQQ/FaceNet

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/LKLQQ/ssc_resnet50

Deep Residual Learning for Image Recognition

https://github.com/LM095/DDPG-implementation

Continuous control with deep reinforcement learning

https://github.com/LONG-9621/Extract_Point_3D

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

https://github.com/LONG-9621/PointNet

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

https://github.com/Latope2-150/Progressive_Growing_of_GANs-PyTorch

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

https://github.com/Ldpe2G/PCANet

PCANet: A Simple Deep Learning Baseline for Image Classification?

https://github.com/LeChangAlex/GAN-Metric

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

https://github.com/LebronGG/PointNet

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

https://github.com/LeeRel1991/SFD

S$^3$FD: Single Shot Scale-invariant Face Detector

https://github.com/Lemoners/Attention-Is-All-You-Need

A Structured Self-attentive Sentence Embedding

https://github.com/LeoToledo/PivotingTaskRL

Reinforcement Learning for Pivoting Task

https://github.com/Lester1711/DSI15_Capstone

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/LetheSec/Fer2013-Facial-Emotion-Recognition-Pytorch

https://github.com/Li-Chongyi/Zero-DCE_extension

Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation

https://github.com/LinRiver/Realtime_facedetection

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Little-Frog-233/imagecluster

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Lmath11/darknet

Focal Loss for Dense Object Detection

https://github.com/Lornatang/ESRGAN-PyTorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

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

Deep Residual Learning for Image Recognition

https://github.com/Lornatang/tensorflow-vgg

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/LostOxygen/poison_froggo

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

https://github.com/Lotayou/everybody_dance_now_pytorch

Everybody Dance Now

https://github.com/LuEE-C/PPO-Keras

Proximal Policy Optimization Algorithms

https://github.com/LucasSilve/openAIgym

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

https://github.com/LuigiRussoDev/Covid19Detection

Deep Residual Learning for Image Recognition

https://github.com/LuigiRussoDev/ResNets

Deep Residual Learning for Image Recognition

https://github.com/Lw510107/pointnet-2018.6.27-

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

https://github.com/M155K4R4/facenet

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/MFajcik/RumourEval2019

A Structured Self-attentive Sentence Embedding

https://github.com/MIC-DKFZ/DetectionAndRegression

Focal Loss for Dense Object Detection

https://github.com/MIC-DKFZ/RegRCNN

Focal Loss for Dense Object Detection

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