Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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보존된 평가 기록

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코드

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

https://github.com/DSL-FIQA/DSL-FIQA

DSL-FIQA: Assessing Facial Image Quality via Dual-Set Degradation Learning and Landmark-Guided Transformer

https://github.com/DableUTeeF/keras-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/Daeijavad/MAD-GAN

Multi-Agent Diverse Generative Adversarial Networks

https://github.com/DaikiTanak/manifold_mixup

Deep Residual Learning for Image Recognition

https://github.com/Daisy-Zhang/Face-X-ray

Face X-ray for More General Face Forgery Detection

https://github.com/DaloroAT/first_break_picking

Focal Loss for Dense Object Detection

https://github.com/Dam930/rllab

Benchmarking Deep Reinforcement Learning for Continuous Control

https://github.com/DamienLancry/blocking_torcs

Simulated Car Racing Championship: Competition Software Manual

https://github.com/DamienLancry/torcs-1.3.4

Simulated Car Racing Championship: Competition Software Manual

https://github.com/DaneyAlex5/Live-Face-Verification-Using-Deep-Learning

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/DaneyAlex5/Webcam-based-Face-Recognition-using-Deep-Learning-

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/DanielLSM/safe-rl-tutorial

Continuous control with deep reinforcement learning

https://github.com/DanielTakeshi/DCUR

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/Danil328/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/DartEnv/dart-env

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

https://github.com/Dascka/dl_q-learning_comparator

Deep Reinforcement Learning that Matters

https://github.com/DataScienceNigeria/D-Kitty-D-Claw.-Robotics-Benchmarks-for-Learning-with-Low-Cost-Robots-

ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots

https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw

FaceForensics++: Learning to Detect Manipulated Facial Images

https://github.com/DavidCastilloAlvarado/Path-planning-and-Reinforcement-Learning

Proximal Policy Optimization Algorithms

https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch

Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions

https://github.com/Deci-AI/super-gradients

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/DeepMark/deepmark

Deep Residual Learning for Image Recognition

https://github.com/Deepest-Project/WorldModels-A3C

World Models

https://github.com/Dekki-Aero/DDPG

Continuous control with deep reinforcement learning

https://github.com/Denver-Pinto/FaceRecognitionAndroid

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation

Focal Loss for Dense Object Detection

https://github.com/DevSlem/AINE-DRL

Proximal Policy Optimization Algorithms

https://github.com/Dictator890/ESRGAN-Android-Demo

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/Dijaq/pain_regression

Regularizing Face Verification Nets For Pain Intensity Regression

https://github.com/DingXiaoH/DiverseBranchBlock

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/DingXiaoH/GSM-SGD

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/DingXiaoH/ResRep

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/Dir-b/PointNet

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

https://github.com/DivJAth/DeepLearning5922

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

https://github.com/DogeZen/resnet2onnx2tensorrt

Deep Residual Learning for Image Recognition

https://github.com/DonCorle0ne/damage_detection

Focal Loss for Dense Object Detection

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