https://github.com/rowerliu/add
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color
FaceNet: A Unified Embedding for Face Recognition and Clustering
Detecting Overfitting of Deep Generative Networks via Latent Recovery
Very Deep Convolutional Networks for Large-Scale Image Recognition
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
Very Deep Convolutional Networks for Large-Scale Image Recognition
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
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.