https://github.com/Mind23-2/MindCode-119
Very Deep Convolutional Networks for Large-Scale Image 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.
Very Deep Convolutional Networks for Large-Scale Image Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
CenterFace: Joint Face Detection and Alignment Using Face as Point
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Deep High-Resolution Representation Learning for Visual Recognition
High-Resolution Representations for Labeling Pixels and Regions
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Real-world adversarial attack on MTCNN face detection system
Very Deep Convolutional Networks for Large-Scale Image Recognition
Deep High-Resolution Representation Learning for Visual Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PyramidBox++: High Performance Detector for Finding Tiny Face
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
RetinaFace: Single-stage Dense Face Localisation in the Wild
SphereFace Revived: Unifying Hyperspherical Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Circle Loss: A Unified Perspective of Pair Similarity Optimization
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PyramidBox++: High Performance Detector for Finding Tiny Face
Very Deep Convolutional Networks for Large-Scale Image Recognition
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.