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/DELTA37/LatentKeypointGAN
https://github.com/DILiS-lab/drivers-of-predictive-aleatoric-uncertainty
FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
https://github.com/DInesh1234-wq/tyredataset
https://github.com/DMarwan/NutricIA
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DSL-FIQA/DSL-FIQA
https://github.com/DableUTeeF/keras-efficientnet
https://github.com/Daeijavad/MAD-GAN
https://github.com/DaikiTanak/manifold_mixup
https://github.com/Daisy-Zhang/Face-X-ray
https://github.com/DaloroAT/first_break_picking
https://github.com/DaneyAlex5/Live-Face-Verification-Using-Deep-Learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/DaneyAlex5/Webcam-based-Face-Recognition-using-Deep-Learning-
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Danil328/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/DataScienceNigeria/Fake-Detection-dataset-for-deepfake-from-Google-and-Jigsaw
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/DeadAt0m/ActiveSparseShifts-PyTorch
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
https://github.com/Deci-AI/super-gradients
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/DeepMark/deepmark
https://github.com/Denver-Pinto/FaceRecognitionAndroid
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/DetectionTeamUCAS/RetinaNet_Tensorflow_Rotation
https://github.com/Dictator890/ESRGAN-Android-Demo
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/Dijaq/pain_regression
Regularizing Face Verification Nets For Pain Intensity Regression
https://github.com/DingXiaoH/DiverseBranchBlock
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/DingXiaoH/GSM-SGD
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/DingXiaoH/ResRep
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/DivJAth/DeepLearning5922
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/DogeZen/resnet2onnx2tensorrt
https://github.com/DonCorle0ne/damage_detection
https://github.com/DongDem/RangeLoss_MarginalLoss_Tensorflow
https://github.com/DongDem/RangeLoss_Marginal_Loss_Tensorflow
https://github.com/Dou-Yu-xuan/FSRNet
FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/Dou-Yu-xuan/vggface2-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/Doyosae/Deep-Complex-Networks
https://github.com/Doyosae/Deep_Complex_Networks
https://github.com/DrMMZ/RetinaNet
https://github.com/DrRyanHuang/VGGNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.