https://github.com/elangarka/Regularized-Xception-FER-Extra-Training-Data-Step-Decay-Learning-Rate
Challenges in Representation Learning: A report on three machine learning contests
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.
Challenges in Representation Learning: A report on three machine learning contests
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
RetinaFace: Single-stage Dense Face Localisation in the Wild
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Deep High-Resolution Representation Learning for Visual Recognition
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
Selective Refinement Network for High Performance Face Detection
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.