https://github.com/polarisZhao/mtcnn-pytorch
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
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/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
https://github.com/ppriyank/Pytorch-Additive_Margin_Softmax_for_Face_Verification
https://github.com/prajinkhadka/face_det_check
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/prakashpandey9/Text-Classification-Pytorch
https://github.com/prakharbhardwaj1/emotion-recognition
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/pranavbudhwant/ACVAEGAN
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
https://github.com/pranavjadhav001/arcface_inversion
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/pshashk/seesaw-facenet
AirFace: Lightweight and Efficient Model for Face Recognition
https://github.com/pterhoer/FaceImageQuality
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/pterhoer/FingerprintImageQuality
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
https://github.com/ptran1203/facial_beauty
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/pulp-platform/hwpe-tb
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulp
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/pulp-platform/pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/purdue-m2/robust_fairness_withNoisy_labels
Analyzing Fairness in Deepfake Detection With Massively Annotated Databases
https://github.com/pwc-1/Paper-9/tree/main/5/wavlm
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/pwc-1/Paper-9/tree/main/6/Zero-DCE%2B%2B
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/pytorch/vision
https://github.com/qbxlvnf11/face-detection-MTCNN
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/qianjinhao/circle-loss
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/qiaoxiu/facenetRetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/qidiso/mobilefacenet-V2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/qingyujean/Classification-on-imbalanced-data
https://github.com/qiuyu96/carver
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
https://github.com/quangson05dt1/keras_facenet_ver1
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/qvdang-dev/Facial-Recognition
https://github.com/qvdang-dev/Facial-Recognizance
https://github.com/rahowa/catboost_focal_loss
https://github.com/rahuls02/Image-Noise-Reduction
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.