https://github.com/aayushdD/Face_recognition-Keras-openface
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
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
High-Resolution Representations for Labeling Pixels and Regions
Training Region-based Object Detectors with Online Hard Example Mining
FaceNet: A Unified Embedding for Face Recognition and Clustering
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
VGGFace2: A dataset for recognising faces across pose and age
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images
Real-time Convolutional Neural Networks for Emotion and Gender Classification
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
VGGFace2: A dataset for recognising faces across pose and age
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FlashFace: Human Image Personalization with High-fidelity Identity Preservation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.