Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning

@article{Van2020InterpretingTL,
  title={Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning},
  author={Toan Pham Van and Tam Minh Nguyen and Ngoc N. Tran and Hoai Viet Nguyen and Linh Doan Bao and Huy Dao Quang and Ta Minh Thanh},
  journal={2020 International Conference on Advanced Computing and Applications (ACOMP)},
  year={2020},
  pages={49-54}
}
With great progress in the development of Generative Adversarial Networks (GANs), in recent years, the quest for insights in understanding and manipulating the latent space of GAN has gained more and more attention due to its wide range of applications. While most of the researches on this task have focused on unsupervised learning method, which induces difficulties in training and limitation in results, our work approaches another direction, encoding human's prior knowledge to discover more… 

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