https://github.com/HarmanDotpy/Normalizations-in-Deep-Learning
Very Deep Convolutional Networks for Large-Scale Image Recognition
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/Harry-Muzart/harry-muzart.github.io
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/HavenFeng/photometric_optimization
https://github.com/Hayashi-Yudai/ML_models
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/HearyShen/HAN-SelfAttentive
https://github.com/Heonozis/FaceShifter-pytorch
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/HiJiGOO/self-attentive
https://github.com/Hikaylee/SynergyNet
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/HongSic/DarknetAI
https://github.com/HongguLiu/MesoNet.Pytorch
https://github.com/HongwenZhang/JVCR-3Dlandmark
https://github.com/Human3DAIGC/AccurateFacialLandmarkDetection
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/IBM/NeuronAlignment
https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/IIGROUP/TediGAN
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/IMvision12/keras-vision-models
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/IPLI/IPLI_Server
https://github.com/IQTLabs/Pytorch_Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/IanSullivan/DeepFakeTorch
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/IcedDoggie/Micro-Expression-with-Deep-Learning
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
https://github.com/IcewineChen/mxnet-batch_hard_triplet_loss
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Ierezell/PapierFewShot
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/IlyaOvodov/AngelinaReader
https://github.com/InterDigitalInc/Latent-Transformer
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/Ipsedo/MusicGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/IsharaSandun/Face-Recognition-using-python
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/IssacCyj/Adversarial-Occlussion-aware-Face-Detection
https://github.com/J-woooo/Mask_Remover
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/JContro/pytorch_projects
https://github.com/JHLee0513/Salt_detection_challenge
https://github.com/JONGSKY/paper
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.