https://github.com/tejaswigowda/semseg-pytorch
High-Resolution Representations for Labeling Pixels and Regions
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.
High-Resolution Representations for Labeling Pixels and Regions
Towards Universal Backward-Compatible Representation Learning
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
Inverse-Category-Frequency based supervised term weighting scheme for text categorization
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
GANimation: Anatomically-aware Facial Animation from a Single Image
CYBORG: Blending Human Saliency Into the Loss Improves Deep Learning
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
RobFR: Benchmarking Adversarial Robustness on Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Training Region-based Object Detectors with Online Hard Example Mining
RetinaFace: Single-stage Dense Face Localisation in the Wild
Unsupervised Learning of Object Landmarks through Conditional Image Generation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.