Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning

• URL: http://arxiv.org/abs/2102.12139v1
• Date: Wed, 24 Feb 2021 09:00:18 GMT
• Title: Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning
• Authors: Toan Pham Van, Tam Minh Nguyen, Ngoc N. Tran, Hoai Viet Nguyen, Linh Bao Doan, Huy Quang Dao and Thanh Ta Minh
• Abstract summary: This paper encodes human's prior knowledge to discover more about the hidden space of GAN. With this supervised manner, we produce promising results, demonstrated by accurate manipulation of generated images. Even though our model is more suitable for task-specific problems, we hope that its ease in implementation, preciseness, robustness, and the allowance of richer set of properties can enhance the result of many current applications.
• Score: 1.231476564107544
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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 about the hidden space of GAN. With this supervised manner, we produce promising results, demonstrated by accurate manipulation of generated images. Even though our model is more suitable for task-specific problems, we hope that its ease in implementation, preciseness, robustness, and the allowance of richer set of properties (compared to other approaches) for image manipulation can enhance the result of many current applications.

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