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Found 5 papers, 4 papers with code
ComboLoss for Facial Attractiveness Analysis with Squeeze-and-Excitation Networks
Loss function is crucial for model training and feature representation learning, conventional models usually regard facial attractiveness recognition task as a regression problem, and adopt MSE loss or Huber variant loss as supervision to train a deep convolutional neural network (CNN) to predict facial attractiveness score.
Multimedia
GL-GAN: Adaptive Global and Local Bilevel Optimization model of Image Generation
Although Generative Adversarial Networks have shown remarkable performance in image generation, there are some challenges in image realism and convergence speed.
ClsGAN: Selective Attribute Editing Model Based On Classification Adversarial Network
In addition, to further improve the transfer accuracy of generated images, an attribute adversarial classifier (referred to as Atta-cls) is introduced to guide the generator from the perspective of attribute through learning the defects of attribute transfer images.
Data-driven Approach for Quality Evaluation on Knowledge Sharing Platform
In recent years, voice knowledge sharing and question answering (Q&A) platforms have attracted much attention, which greatly facilitate the knowledge acquisition for people.
Transferring Rich Deep Features for Facial Beauty Prediction
Feature extraction plays a significant part in computer vision tasks.
Ranked #1 on
Facial Beauty Prediction
on SCUT-FBP
