https://github.com/sobieskibj/rcsb
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
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
FaceNet: A Unified Embedding for Face Recognition and Clustering
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
An Embarrassingly Simple Approach for Knowledge Distillation
Deforming Autoencoders: Unsupervised Disentangling of Shape and Appearance
Progressive Growing of GANs for Improved Quality, Stability, and Variation
High-Resolution Representations for Labeling Pixels and Regions
FaceNet: A Unified Embedding for Face Recognition and Clustering
Progressive Growing of GANs for Improved Quality, Stability, and Variation
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
Real-time Convolutional Neural Networks for Emotion and Gender Classification
FaceNet: A Unified Embedding for Face Recognition and Clustering
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.