Dahuin Jung

HexaGAN: Generative Adversarial Nets for Real World Classification

2019 · arXiv (Cornell University)

Most deep learning classification studies assume clean data. However, when dealing with the real world data, we encounter three problems such as 1) missing data, 2) class imbalance, and 3) missing label problems. These problems …