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/modelhub-ai/vgg-19
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/modestyachts/ImageNetV2
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohaEs/Train-Predict-Landmarks-by-flat-net
Automatic vocal tract landmark localization from midsagittal MRI data
https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mohammadimathstar/grlgq
https://github.com/mohanliu/HRnet-semantic-segmentation-exp
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/mohitwildbeast/Facial-Recognition-Using-FaceNet-Siamese-One-Shot-Learning
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/moron96/FaceRecogLDAP
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mortezagolzan/Face-Anti-Spoofing
https://github.com/mowshon/lipsync
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/mr-bulb/DL_basic_github
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/mrpintime/Face-Liveness-TensorFlow
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/mrv8x/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/mrw-soumik/Tiny-Faces-in-Tensorflow
https://github.com/mrzzy/np-dl-assign-1
https://github.com/msaadsaeed/sbnet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mseg-dataset/mseg-semantic
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/mshehrozsajjad/Age-Classification
Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/mszpc/facenet_ascend
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/mszpc/resnet18
https://github.com/mundanePeo/faceRecognition
https://github.com/mushfiqurrahman250/Face-Recognition-master
https://github.com/musifahamran/FYP
https://github.com/musikisomorphie/swd
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/muzairkhattak/multimodal-prompt-learning
https://github.com/muzairkhattak/protext
https://github.com/nabarunbaruaAIML/ATCC_Yolov5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nagadomi/kaggle-cifar10-torch7
https://github.com/namdvt/Focal-loss-pytorch-implementation
https://github.com/nannau/DoWnGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nanzhaogang/contrib/tree/master/application/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/nashory/pggan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/natel9178/transformer-refork
https://github.com/naver-ai/StyleMapGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/naykun/tf_pg_gans
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.