https://github.com/saimj7/Action-Recognition-in-Real-Time
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
Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
L2-constrained Softmax Loss for Discriminative Face Verification
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
Very Deep Convolutional Networks for Large-Scale Image Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
High-Resolution Representations for Labeling Pixels and Regions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.