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

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보존된 평가 기록

3

코드

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

https://github.com/Hayashi-Yudai/ML_models

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/Hazqeel09/ellzaf_ml

S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens

https://github.com/HearyShen/HAN-SelfAttentive

A Structured Self-attentive Sentence Embedding

https://github.com/Heonozis/FaceShifter-pytorch

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

https://github.com/HiJiGOO/self-attentive

A Structured Self-attentive Sentence Embedding

https://github.com/Hikaylee/SynergyNet

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

https://github.com/HongSic/DarknetAI

Focal Loss for Dense Object Detection

https://github.com/HongguLiu/MesoNet.Pytorch

MesoNet: a Compact Facial Video Forgery Detection Network

https://github.com/HongwenZhang/JVCR-3Dlandmark

https://github.com/Human3DAIGC/AccurateFacialLandmarkDetection

Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection

https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/HzcIrving/DLRL-PlayGround

Distributed Distributional Deterministic Policy Gradients

https://github.com/HzcIrving/DLRL-PlayGround/tree/main/Offline%20RL/BCQ

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/IBM/NeuronAlignment

Deep Residual Learning for Image Recognition

https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

https://github.com/IIGROUP/TediGAN

TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

https://github.com/IMvision12/keras-vision-models

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/IPLI/IPLI_Server

Focal Loss for Dense Object Detection

https://github.com/IQTLabs/Pytorch_Retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/IanSullivan/DeepFakeTorch

DeepFaceLab: Integrated, flexible and extensible face-swapping framework

https://github.com/IcedDoggie/Micro-Expression-with-Deep-Learning

Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition

https://github.com/IcewineChen/mxnet-batch_hard_triplet_loss

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Ierezell/PapierFewShot

Few-Shot Adversarial Learning of Realistic Neural Talking Head Models

https://github.com/IlyaOvodov/AngelinaReader

Focal Loss for Dense Object Detection

https://github.com/InSpaceAI/RL-Zoo

Continuous control with deep reinforcement learning

https://github.com/InterDigitalInc/Latent-Transformer

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

https://github.com/Ipsedo/EvoMotion

Proximal Policy Optimization Algorithms

https://github.com/Ipsedo/MusicGAN

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

https://github.com/IsharaSandun/Face-Recognition-using-python

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/IssacCyj/Adversarial-Occlussion-aware-Face-Detection

Adversarial Occlusion-aware Face Detection

https://github.com/Itomigna2/Muesli-lunarlander

Muesli: Combining Improvements in Policy Optimization

https://github.com/IvanVigor/Deep-Deterministic-Policy-Gradient-Unity-Env

Continuous control with deep reinforcement learning

https://github.com/IvanVigor/MADDPG-Unity

Continuous control with deep reinforcement learning

https://github.com/J-woooo/Mask_Remover

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

https://github.com/J93T/TP4-DDPG

Continuous control with deep reinforcement learning

https://github.com/JContro/pytorch_projects

Deep Residual Learning for Image Recognition

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