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/Jagannathrk2020/Face-detection-and-counting-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/JaidedAI/EasyOCR
https://github.com/James-Gilbert-/medical-deep-learning
https://github.com/James-Gilbert-/pneumonia-detection
https://github.com/JanFschr/DeepFaceLabTest
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/JanMarcelKezmann/Residual-Network-Architectures
https://github.com/JanMarcelKezmann/TensorFlow-Advanced-Segmentation-Models
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/Jarr0d/Human-Parsing-Network
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/JaryHuang/awesome_SSD_FPN_GIoU
https://github.com/JayPatwardhan/ResNet-PyTorch
https://github.com/JeansonYang/VGG16-MindSpore
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Jeongyun-Lee-0423/Classification
https://github.com/JesonZhang822/Dogs-VS-Cats-Project
https://github.com/JeyesHan/P2LR
https://github.com/JiangShaoYin/DAN
Deep Alignment Network: A convolutional neural network for robust face alignment
https://github.com/Jo-dsa/SemanticSeg
https://github.com/JoeTracey/Portfolio
https://github.com/Johnny952/retinaface_mod
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/JonasGeiping/poisoning-gradient-matching
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks
https://github.com/Jopapy19/CNN-App
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/JoseLGP/FaceRecognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/JoyLuo/face-attribute-recognition-paper-list
Deep Imbalanced Learning for Face Recognition and Attribute Prediction
https://github.com/JuanCasian/ML-Speech-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KLT20/Realtime-Face-Emotion-Recognition
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/KSolves-ML/Face-Reco-Flask
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/KabirSingh114/DeepFake_Face_Detection
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
https://github.com/Kailianghu/A-Structured-Self-Attentive-Sentence-Embedding
https://github.com/KaimingHe/deep-residual-networks
https://github.com/KaitavMehta95/Insight-Ganscape-Studio
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/Kalyan0821/NST
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kalyan0821/myelinfoundry-challenge
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/KarT1994/EmnistRecognizer
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Kareemcc/FaceMaskDetection
https://github.com/KarimArmanious/Age-Net
Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation
https://github.com/KarimaCha/CNN_Trust_Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.