https://github.com/Eltomad/insightface
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
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.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Multi-view Face Detection Using Deep Convolutional Neural Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.