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/bobetocalo/bobetocalo_eccv18
Face Alignment using a 3D Deeply-initialized Ensemble of Regression Trees
https://github.com/bobetocalo/bobetocalo_prl19
https://github.com/bogireddytejareddy/micro-expression-recognition
https://github.com/borhanMorphy/fastface
https://github.com/borhanMorphy/light-face-detection
https://github.com/boris127/vehicle-detection
https://github.com/bovifocr/insightface
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/bruceeeee/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/bruinxiong/Evaluation_IJBA
A Good Practice Towards Top Performance of Face Recognition: Transferred Deep Feature Fusion
https://github.com/brunocalogero/AMLSassignment
From Facial Parts Responses to Face Detection: A Deep Learning Approach
https://github.com/bryandlee/FreezeG
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/brycexu/MR-Residual-Net
https://github.com/bubbliiiing/retinaface-keras
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/buptdbj/darknet-windows-linux
https://github.com/busyboxs/Some-resources-useful-for-me
Training Region-based Object Detectors with Online Hard Example Mining
https://github.com/bxz9200/ultraclean
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition
https://github.com/c1ph3rr/Very-Deep-Convolutional-Networks-For-Large-Scale-Image-Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/caihaocong/Yolov3_AlexeyAB_Version
https://github.com/caihuaiguang/CHG-Shapley-for-Data-Selection
https://github.com/calmisential/TensorFlow2.0_Image_Classification
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/calmisential/TensorFlow2.0_ResNet
https://github.com/camel007/caffe-moon
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
https://github.com/cameronchoi/r3det-copy
https://github.com/canturan10/light_side
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/canturan10/satellighte
https://github.com/caojoshua/Plastic-Recyclability
https://github.com/carlosb1/upc-aidl-19-team4
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/carolgithubv1/convnets-keras
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/cat-claws/RobFace
https://github.com/cc-hpc-itwm/DeepFakeDetection
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/cchinchristopherj/Right-Whale-Unsupervised-Model
https://github.com/cecile-hi/regularized-adaptive-weight-modification
What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection
https://github.com/cedricoeldorf/ConditionalStyleGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/celsolbm/Attention_Text
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.