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/moreanp/csro

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

https://github.com/morikatron/GAIL_PPO

Proximal Policy Optimization Algorithms

https://github.com/morikatron/PPO

High-Dimensional Continuous Control Using Generalized Advantage Estimation

https://github.com/moron96/FaceRecogLDAP

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/mortezagolzan/Face-Anti-Spoofing

Learning Generalized Spoof Cues for Face Anti-spoofing

https://github.com/mosmos/face_key_point_detection

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/mowshon/lipsync

A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild

https://github.com/mr-bulb/DL_basic_github

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mrpintime/Face-Liveness-TensorFlow

Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection

https://github.com/mrv8x/DeepFaceLab

DeepFaceLab: Integrated, flexible and extensible face-swapping framework

https://github.com/mrw-soumik/Tiny-Faces-in-Tensorflow

Can we still avoid automatic face detection?

https://github.com/mrzzy/np-dl-assign-1

Deep Residual Learning for Image Recognition

https://github.com/msaadsaeed/sbnet

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/mseg-dataset/mseg-semantic

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mshehrozsajjad/Age-Classification

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/mszpc/facenet_ascend

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/mszpc/resnet18

Deep Residual Learning for Image Recognition

https://github.com/mundanePeo/faceRecognition

Semi-Siamese Training for Shallow Face Learning

https://github.com/mushfiqurrahman250/Face-Recognition-master

Network In Network

https://github.com/musifahamran/FYP

Network In Network

https://github.com/musikisomorphie/swd

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

https://github.com/muzairkhattak/multimodal-prompt-learning

Learning to Prompt for Vision-Language Models

https://github.com/muzairkhattak/protext

Learning to Prompt for Vision-Language Models

https://github.com/mxblr/DeepRLHockey

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

https://github.com/myo-manipulation/spasticmyoelbow

MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

https://github.com/myohub/myosuite

MyoSuite -- A contact-rich simulation suite for musculoskeletal motor control

https://github.com/nabarunbaruaAIML/ATCC_Yolov5

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/nagadomi/kaggle-cifar10-torch7

Network In Network

https://github.com/nagisazj/idaq_public

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

https://github.com/namdvt/Focal-loss-pytorch-implementation

Focal Loss for Dense Object Detection

https://github.com/nannau/DoWnGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/nanzhaogang/contrib/tree/master/application/circle-loss

Circle Loss: A Unified Perspective of Pair Similarity Optimization

https://github.com/naruya/vgan-pytorch

Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow

https://github.com/nashory/pggan-pytorch

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

https://github.com/natel9178/transformer-refork

A Structured Self-attentive Sentence Embedding

https://github.com/nav74neet/ddpg4biped

Continuous control with deep reinforcement learning

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