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/s3nh/unet-midv500
https://github.com/saber2011/darknet
https://github.com/saednasir/Helmet_Yolo
https://github.com/sagnik1511/Transfer-Learning-with-Python
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sagpant/3dmm_cnn
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
https://github.com/sahilunagar/VGG16-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/saiamrit/Facenet-pytorch
https://github.com/saic-fi/bayesian-prompt-learning
https://github.com/saikrishnadas/ResNet-Pytorch
https://github.com/saimj7/Action-Recognition-in-Real-Time
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sainimohit23/FaceNet-Real-Time-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/saiteja-talluri/Speech2Face
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
https://github.com/sajjjadayobi/FaceLib
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/saloni1998/Cifar-10
https://github.com/samisoto/keras_cosine_based_loss
L2-constrained Softmax Loss for Discriminative Face Verification
https://github.com/sammyamajumdar/ResNet50
https://github.com/samsonadmin/modified-alexeyab-darknet
https://github.com/samuelyu2002/PFLD
https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensorflow
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/sanaullah-ashfat/Face-recognition-with-Arcface-Tensowflow-
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/sandipsahajoy/Predicting-Lymphoma-using-CNN-in-Keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sangHa0411/CIFAR100
https://github.com/sangHa0411/ResNetCIFAR100
https://github.com/sanghyun-son/clustering-kernels
https://github.com/sangyun884/Face2Webtoon
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/sangyun884/WebtoonFaces
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/sankn123/Very-deep-convolutional-networks-for-large-scale-image-recognition-Implementation
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sanoyo/dl-model
https://github.com/sarthakbaiswar/Artistic-Styling
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/say2sarwar/DeepAcid
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Adaptive-Gradient-Clipping
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/sayakpaul/Revisiting-Pooling-in-CNNs
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/sayannath/Zero-DCE-TFLite
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/sberbank-ai/DigiTales
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/sberbank-ai/DigiTeller
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.