https://github.com/mlpc-ucsd/PRTR
Deep High-Resolution Representation Learning for Visual 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.
Deep High-Resolution Representation Learning for Visual Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Representation Learning with Statistical Independence to Mitigate Bias
Real-time Convolutional Neural Networks for Emotion and Gender Classification
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Automatic vocal tract landmark localization from midsagittal MRI data
Very Deep Convolutional Networks for Large-Scale Image Recognition
High-Resolution Representations for Labeling Pixels and Regions
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceNet: A Unified Embedding for Face Recognition and Clustering
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
Rank consistent ordinal regression for neural networks with application to age estimation
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.