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/GXU-GMU-MICCAI/PGAN-Paddle
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/GXYM/Focal-loss
https://github.com/GabrieleMonte/CoMBInE
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gahyeonkim09/AAPL
https://github.com/GangmingZhao/GraphFPN-Graph-Feature-Pyramid-Network-for-Object-Detection
https://github.com/GeorgeLekala/ML
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
https://github.com/GerardLiu96/FECNet
https://github.com/GilLevi/AgeGenderDeepLearning
https://github.com/Glp91/fire-detection
https://github.com/Godot-yry/VGG16_mindspore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gojay001/toolkit-DeepLearning
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Gowti-AiboT/GAN-based-SISR-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Graylab/deepH3-distances-orientations
https://github.com/Gryffindor112358/Arcface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Guillem96/efficient-net-tf
https://github.com/Guillem96/efficientdet-tf
https://github.com/GuoQuanhao/RetinaFace-Paddle
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/GuoQuanhao/Tiny-Face-Paddle
https://github.com/GuohongLi/face-alignment-pytorch
https://github.com/GurpartapSS/Realtime_FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/HHTseng/video-classification
https://github.com/HKUST-NISL/Multitask-Emotion-Recognition-with-Incomplete-Labels
https://github.com/HRNet/HRNet-FCOS
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Facial-Landmark-Detection
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Human-Pose-Estimation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Image-Classification
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Object-Detection
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Semantic-Segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Halesu/4th-ML100Days
https://github.com/HamadYA/GhostFaceNets
https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi
https://github.com/Hamiltonsjtu/based_on_facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/HannH/DMFN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.