Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

3

코드

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

https://github.com/vinayakkailas/Face_Recognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/vincent-thevenin/Realistic-Neural-Talking-Head-Models

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/vincenthanna/facenet_on_mnist

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/vineeths96/Gradient-Compression

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/vineeths96/heterogeneous-systems

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/vinits5/learning3d

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

https://github.com/vinod377/STN-OCR

Deep Residual Learning for Image Recognition

https://github.com/vinod377/STN-OCR-Tensorflow

Deep Residual Learning for Image Recognition

https://github.com/vinx-2105/madgan-degan

Multi-Agent Diverse Generative Adversarial Networks

https://github.com/vipermu/ganimation

GANimation: Anatomically-aware Facial Animation from a Single Image

https://github.com/visionNoob/hrnet_pytorch

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/vita-epfl/rock-pytorch

Deep Residual Learning for Image Recognition

https://github.com/vitoralbiero/face_analysis_pytorch

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/vladimirwest/insightface_cinematic

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/vlievin/Unet

Deep Residual Learning for Image Recognition

https://github.com/vnbot2/arcface

SphereFace: Deep Hypersphere Embedding for Face Recognition

https://github.com/vohoaiviet/sphereface

SphereFace: Deep Hypersphere Embedding for Face Recognition

https://github.com/voot-t/ril_co

Robust Imitation Learning from Noisy Demonstrations

https://github.com/voqtuyen/CDCN-Face-Anti-Spoofing.pytorch

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

https://github.com/vu-resilient-distributed-systems/assignment-1-fall-19

Simulated Car Racing Championship: Competition Software Manual

https://github.com/vub-ai-lab/qwr

Q-Value Weighted Regression: Reinforcement Learning with Limited Data

https://github.com/vuongtrannguyenkhoi/darknet

Focal Loss for Dense Object Detection

https://github.com/vuthede/heatmap-based-landmarker

Heatmap Regression via Randomized Rounding

https://github.com/vvanirudh/imitation-learning-gym

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

https://github.com/vwegn/dm

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/w-sugar/prtr

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/w510056105/DeepLearning

MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices

https://github.com/wanderine/ProgressiveGAN3D

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

https://github.com/wangjk666/PyDeepFakeDet

M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection

https://github.com/wangshanmin/interface

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/wangshub/RL-Stock

Proximal Policy Optimization Algorithms

https://github.com/wangx404/script_for_downloading_IMDb_face

The Devil of Face Recognition is in the Noise

https://github.com/wangxianhao/Improved-YOLOv3-for-UAV

Focal Loss for Dense Object Detection

https://github.com/wangyuan249/Mymmt767

Deep Residual Learning for Image Recognition

https://github.com/wangzhengfei0730/NIPS2018-AIforProsthetics

Proximal Policy Optimization Algorithms

https://github.com/wanjinchang/MTCNN_USE_TF_E2E

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

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