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/aoru45/Deep-Anomaly-Detection-for-Generalized-Face-Anti-Spoofing

Deep Anomaly Detection for Generalized Face Anti-Spoofing

https://github.com/aoru45/LFFD-Pytorch

LFFD: A Light and Fast Face Detector for Edge Devices

https://github.com/apaz-cli/ML-ImageHash

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/apbose/RLproject_AutonomousNavigation_Torcs

Simulated Car Racing Championship: Competition Software Manual

https://github.com/apisarek/gan_advances

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

https://github.com/aporw/Class-Attendance-Tool--Face-Recognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/apourchot/ERL

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/apourchot/ERL-pytorch

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/apourchot/EvoLearn

Evolution-Guided Policy Gradient in Reinforcement Learning

https://github.com/araffin/sbx

Proximal Policy Optimization Algorithms

https://github.com/arahosu/musicslots

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/aralhekimoglu/face

Sample and Computation Redistribution for Efficient Face Detection

https://github.com/aravindsrinivas/curl_rainbow

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/arghadeep25/Face-Detection

Max-Margin Object Detection

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/ariel415el/ALAE

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

https://github.com/ariel415el/SimplePytorch-ALAE

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

https://github.com/arkel23/Danbooru2018AnimeCharacterRecognitionDataset_Revamped

DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition

https://github.com/arnomoonens/yarll

Continuous control with deep reinforcement learning

https://github.com/arrival-ltd/catalyst-rl-tutorial

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/arsfutura/face-recognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/artemmavrin/focal-loss

Focal Loss for Dense Object Detection

https://github.com/arturjordao/arturjordao

Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method

https://github.com/artxtech/darknet-rnn

Focal Loss for Dense Object Detection

https://github.com/artynet/darknet-alexeyAB

Focal Loss for Dense Object Detection

https://github.com/arunavkonwar/world-models

World Models

https://github.com/ashaaher/Reinforcement-Learning-Project

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/ashawkey/RAD-NeRF

Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/ashokpant/video_processing_opencv_cpp

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

https://github.com/ashushekar/VGG16

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ashutosh1919/FaceGenerationStyleGAN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/asmadotgh/unc-net

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/asparius/barlowrl

CURL: Contrastive Unsupervised Representations for Reinforcement Learning

https://github.com/aspk/Tensorflow_Deep_RL

Parameter Space Noise for Exploration

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

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