Papers with Code 아카이브
Facial Recognition and Modelling
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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/oawiles/FAb-Net
Self-supervised learning of a facial attribute embedding from video
https://github.com/obj2vec/obj2vec
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/oellop/facial_expression
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/okeer/nst-tele-bot
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oktapodi/ps_resNet
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/olaviinha/NeuralImageSuperResolution
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/olegpolivin/antisleep
https://github.com/olgi9911/MMDG
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
https://github.com/ollewelin/libtorch-GPU-CNN-test-MNIST-with-Batchnorm
https://github.com/omaarelsherif/Face-Detection-Using-Deep-Learning
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omaarelsherif/Face-Detection-Using-MTCNN
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/omarsayed7/Deep-Emotion
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
https://github.com/omidmn/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omidnezami/Engagement-Recognition
Automatic Recognition of Student Engagement using Deep Learning and Facial Expression
https://github.com/omkarghugarkar007/Neural_Super_Sampling
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/omrijsharon/torchlex
https://github.com/omron-sinicx/sbsfigures
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
https://github.com/ongyongzheng/generative_encoder
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/onion-liu/aahq-dataset
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
https://github.com/onwan946/DeepFaceLab
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/oorrppp2/darknet-pose
https://github.com/open-mmlab/mmclassification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmdetection
https://github.com/open-mmlab/mmgeneration
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/open-mmlab/mmpose
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/open-mmlab/mmsegmentation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/opencv/opencv_zoo/tree/master/models/face_recognition_sface
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/oskyhn/CNNs-Without-Borders
https://github.com/osmr/imgclsmob
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/oxygen0605/ImageClassification
https://github.com/ozora-ogino/LCNN
https://github.com/p0werweirdo/tagfcl
Compacting, Picking and Growing for Unforgetting Continual Learning
https://github.com/paarthneekhara/adversarialdeepfakes
Adversarial Deepfakes: Evaluating Vulnerability of Deepfake Detectors to Adversarial Examples
https://github.com/pacifinapacific/StyleGAN_LatentEditor
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/paddorch/SelfAttnSent.paddle
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.