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/human-analysis/secure-face-matching

Secure Face Matching Using Fully Homomorphic Encryption

https://github.com/humbleOldSage/Triplet-Loss-for-Personal-dataset

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/hustic/TinyImageSet

Deep Residual Learning for Image Recognition

https://github.com/hustvl/sparseinst

Deep Residual Learning for Image Recognition

https://github.com/huybik/GAN-Experiments-Pytorch

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

https://github.com/huybik/GANs-Pytorch

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

https://github.com/huyhieupham/3D-Face-Recognition

Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

https://github.com/huytranvan2010/RetinaNet-Understanding

Focal Loss for Dense Object Detection

https://github.com/hweersot/darknet-custom

Focal Loss for Dense Object Detection

https://github.com/hx19940102/Image-Segmentation-based-on-FCN

Deep Residual Learning for Image Recognition

https://github.com/hyang0129/foodclassapp

Focal Loss for Dense Object Detection

https://github.com/hybridrobotics/car-racing

Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

https://github.com/hycis/TensorGraph

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/hyhchaos/FakePolisher

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

https://github.com/hysts/pytorch_resnet

Deep Residual Learning for Image Recognition

https://github.com/hysts/pytorch_resnet_preact

Deep Residual Learning for Image Recognition

https://github.com/hyunin-lee/ForecasterSAC

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

https://github.com/hz-ants/pointnet.pytorch

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

https://github.com/hz2538/iot_JennyGo

Deep Residual Learning for Image Recognition

https://github.com/hzh8311/pyhowfar

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

https://github.com/hzlsaber/sida

SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model

https://github.com/i6092467/diff-bias-proxies

Intra-Processing Methods for Debiasing Neural Networks

https://github.com/iArunava/ResNet

Deep Residual Learning for Image Recognition

https://github.com/iChenning/facedetection

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/iDataist/Continuous-Control-with-Deep-Deterministic-Policy-Gradient

Continuous control with deep reinforcement learning

https://github.com/iDataist/Tennis-With-Multi-Agent-Reinforcement

Continuous control with deep reinforcement learning

https://github.com/iPRoBe-lab/semi-adversarial-networks

Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

https://github.com/iacopomasi/face_specific_augm

FacePoseNet: Making a Case for Landmark-Free Face Alignment

https://github.com/iamgroot42/A-LINK

https://github.com/ibadr/RetinaFace

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/ibbu10/Facial-Landmark-Detection-Using-CNN

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/ibendrup/MulticolumnNetwork

Multicolumn Networks for Face Recognition

https://github.com/icey-zhang/MTCNN_paddle

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

https://github.com/icey-zhang/RetinaFace_paddle

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/iclavera/learning_to_adapt

Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

https://github.com/idealo/image-super-resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

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