https://github.com/kaseris/ILSVRCPlus
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
Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network
Very Deep Convolutional Networks for Large-Scale Image Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
High-Resolution Representations for Labeling Pixels and Regions
Very Deep Convolutional Networks for Large-Scale Image Recognition
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
Simultaneous face detection and 360 degree headpose estimation
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
High-Resolution Representations for Labeling Pixels and Regions
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Deep High-Resolution Representation Learning for Visual Recognition
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Progressive Growing of GANs for Improved Quality, Stability, and Variation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.