https://github.com/setharram/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Facial tasks in machine learning operate based on images or video frames (or other datasets) focussed on human faces.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Island Loss for Learning Discriminative Features in Facial Expression Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model
FaceNet: A Unified Embedding for Face Recognition and Clustering
GANimation: Anatomically-aware Facial Animation from a Single Image
Deep High-Resolution Representation Learning for Visual Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
RetinaFace: Single-stage Dense Face Localisation in the Wild
Deep High-Resolution Representation Learning for Visual Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.