DefogGAN: Predicting Hidden Information in the StarCraft Fog of War with Generative Adversarial Nets

Abstract

We propose DefogGAN, a generative approach to the problem of inferring state information hidden in the fog of war for real-time strategy (RTS) games. Given a partially observed state, DefogGAN generates defogged images of a game as predictive information. Such information can lead to create a strategic agent for the game. DefogGAN is a conditional GAN variant featuring pyramidal reconstruction loss to optimize on multiple feature resolution scales. We have validated DefogGAN empirically using a large dataset of professional StarCraft replays. Our results indicate that DefogGAN can predict the enemy buildings and combat units as accurately as professional players do and achieves a superior performance among state-of-the-art defoggers.

Deep learning approaches for object recognition in plant diseases: a review

“…Zeng et al [144] proposed an illumination-adaptive network for person detection, which is able to eliminate the impact of illumination discrepancy. Besides using operators and filters, some generative-based approaches are applied [145][146][147][148] . Although these works enhance the performance of CNN models on foggy images, there is still room for improvement.…”