  This paper introduces a novel approach for unsupervised object
co-localization using Generative Adversarial Networks (GANs). GAN is a powerful
tool that can implicitly learn unknown data distributions in an unsupervised
manner. From the observation that GAN discriminator is highly influenced by
pixels where objects appear, we analyze the internal layers of discriminator
and visualize the activated pixels. Our important finding is that high image
diversity of GAN, which is a main goal in GAN research, is ironically
disadvantageous for object localization, because such discriminators focus not
only on the target object, but also on the various objects, such as background
objects. Based on extensive evaluations and experimental studies, we show the
image diversity and localization performance have a negative correlation. In
addition, our approach achieves meaningful accuracy for unsupervised object
co-localization using publicly available benchmark datasets, even comparable to
state-of-the-art weakly-supervised approach.
