Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/eewindfly/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/egeoguzman/Liveness-Detection-Siamese
Face Liveness Detection Based on Client Identity Using Siamese Network
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/eladrich/pix2vertex.pytorch
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
https://github.com/elangarka/Regularized-Xception-FER-Extra-Training-Data-Step-Decay-Learning-Rate
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/elhamod/phylonn
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elliottzheng/face-detection
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/eltechno/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/embracesource-cv-com/FCHD
https://github.com/emilianavt/OpenSeeFace
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
https://github.com/enzomuschik/distilfnd
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
https://github.com/eric-erki/ID-Card-Segmentation
https://github.com/eric-erki/IdenProf
https://github.com/eric-erki/Visualising-Facial-Expression-Changes
https://github.com/eric-erki/darknet
https://github.com/eric-erki/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/eric-erki/yolov3
https://github.com/eric-mitchell/mend
https://github.com/eric-mitchell/serac
https://github.com/eriklindernoren/PyTorch-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ermuur/darknet
https://github.com/esadsetts/Mod_4_project
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/esimov/pigo
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/espectre/GCNs_on_imbalanced_datasets
https://github.com/espressif/esp-who
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/eth-sri/llm-anonymization
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/evtimovi/foggysight
https://github.com/ewinata/CIFAR-10
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.