https://github.com/mindspore-ai/models/blob/master/research/cv/PGAN/
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
RetinaFace: Single-stage Dense Face Localisation in the Wild
CenterFace: Joint Face Detection and Alignment Using Face as Point
RetinaFace: Single-stage Dense Face Localisation in the Wild
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Deep High-Resolution Representation Learning for Visual Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Deep High-Resolution Representation Learning for Visual Recognition
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
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
Automatic vocal tract landmark localization from midsagittal MRI data
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
A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.