https://github.com/mdda/deep-learning-models
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
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
Deep High-Resolution Representation Learning for Visual Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Classification is a Strong Baseline for Deep Metric Learning
Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
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.