Abstract
Abstract: This work diverts from the usual by using Generative Adversarial Networks (GANs) to intentionally create flawed yet captivating images. We introduce a controlled chaos system, where a stalling generator network and a disoriented discriminator network co-exist. This strange training method encourages the development of peculiar irregularities within semi-realistic images. We welcome the unpredictability that arises from this chaos, purposely avoiding issues like mode collapse. Moreover, the project delves into a new evaluation paradigm for evaluating these deliberately flawed images. We go beyond typical metrics, exploring the use of faulty metrics and questionable visual inspection techniques. In addition, we investigate the impact of clumsy dataset selection, potentially employing unconventional or tampered datasets to train the GAN for specific types of errors. This work explores the potential of GANs for jumbled up art creation, data manipulation, and image confusion, opening a new frontier in GAN applications. While potentially causing confusion in the field, this research also reveals the unimaginable artistic and data exploration capabilities of GANs.
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