Papers with Code 아카이브
Facial Recognition and Modelling
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 26
- 평가표
- 0
- 보존된 평가 기록
- 0
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/pengrenlai/RetinaNet-tensorflow
https://github.com/pengsongyou/OMG-ADSC
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/penincillin/DREAM
Pose-Robust Face Recognition via Deep Residual Equivariant Mapping
https://github.com/peterhan91/thorax_gan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/petermankowski510/facenet_test
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/peteryuX/arcface-tf2
https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/peteryuX/retinaface-tf2
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/petrum01/Capstone_project_object_detection
https://github.com/pgtinsley/insightface_oge
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/phamquiluan/ResidualMaskingNetwork
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/phillity/BioCapsule
https://github.com/phiresky/pandoc-url2cite
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/phongdinhv/triplet-loss-keras-mnist
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/phuijse/MATIC
https://github.com/piaohe111/gittest
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/pierluigiferrari/ssd_keras
https://github.com/pikabite/segmentations_tf2
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/pikkaay/efficientnet_gpu
https://github.com/piotrkawa/audio-deepfake-source-tracing
https://github.com/pipidog/CNLP
https://github.com/playerkk/face-py-faster-rcnn
https://github.com/pocheck-v2/Pocheck-V2-Beta
https://github.com/podgorskiy/StyleGan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/pohlinwei/AComPianist
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pohwa065/SRGAN-for-Super-Resolution-and-Image-Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/polarisZhao/PFLD-pytorch
https://github.com/polarisZhao/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/polimi-ispl/icpr2020dfdc
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/politecat314/MEBeauty_CNN
https://github.com/pothabattulasantosh/Detection-of-face-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pothabattulasantosh/Detection-of-facial-Manipulated-videos
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/pouya-parsa/progressive_gan
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ppokhare/Handling-Imbalanced-Dataset-for-classification-task
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.