Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/mapooon/PetFace

PetFace: A Large-Scale Dataset and Benchmark for Animal Identification

https://github.com/maragori/DeepfakeForensics-v1

MesoNet: a Compact Facial Video Forgery Detection Network

https://github.com/marcin-laskowski/Pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/marcuspearce/MP_FairFace

FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age

https://github.com/mark-gluzman/NmodelPPO

Proximal Policy Optimization Algorithms

https://github.com/markub3327/rl-agent

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/markub3327/rl-baselines

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/markub3327/rl-toolkit

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/marload/ConvNets-TensorFlow2

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/marload/DeepRL-TensorFlow2

Continuous control with deep reinforcement learning

https://github.com/marmara-machine-learning/Facial_Keypoints_NaimishNet_GPU

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/martin220485/everybody_dance_now_pytorch

Everybody Dance Now

https://github.com/masoudnick/brain-tumor-mri-classification

Deep Residual Learning for Image Recognition

https://github.com/massquantity/DBRL

Continuous control with deep reinforcement learning

https://github.com/matakshay/Neural_Image_Caption_Generator

Deep Residual Learning for Image Recognition

https://github.com/matejker/controllability-of-complex-networks

A Tour of Reinforcement Learning: The View from Continuous Control

https://github.com/mathformoso/RL-playground---ddpg

Continuous control with deep reinforcement learning

https://github.com/mathieuorhan/darknet

Focal Loss for Dense Object Detection

https://github.com/matlab-deep-learning/mtcnn-face-detection

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

https://github.com/matt-roz/progressive_growing_of_gans_tensorflow_2

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

https://github.com/matthewsparr/Reinforcement-Learning-Lesson

Continuous control with deep reinforcement learning

https://github.com/maximerobeyns/taylortd

How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization

https://github.com/maxmax1992/WGAN_WGAN-GP

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

https://github.com/maxwelltsai/DeepGalaxy

Deep Residual Learning for Image Recognition

https://github.com/mayureshpatil30/test-image

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/maziarg/PrivAttack-BCQ

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/mazpie/mastering-urlb

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/mbhenaff/EEN

Query-Efficient Imitation Learning for End-to-End Autonomous Driving

https://github.com/mbrucker07/experiments

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

https://github.com/mbsariyildiz/resnet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/mcbuehler/DeepSEE

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

https://github.com/mdda/deep-learning-models

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mdsarfarazulh/face-verification-facenet-mtcnn

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/mdt48/semantic-segmentation-pytorch

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mdv3101/darknet-yolov3

Focal Loss for Dense Object Detection

https://github.com/mees/Adversarial-Skill-Networks

Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video

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