CycleGAN Makeover (2017) In CycleGAN Makeover, Klingemann turns the emerging tool of CycleGAN toward questions of portraiture, identity, and machine bias. Created in 2017, this series explores the AI-mediated transformation of gender in photographic images - a kind of automated drag act performed by neural networks. CycleGAN, a new generative model that learns to translate images from one domain to another without paired examples, provided the flexibility to reimagine gender. Seizing on this potential, Klingemann trained CycleGAN on two carefully prepared domains: one of female portraits and one of male portraits. By teaching the system to convert an image from one set to the other, he created a model capable of morphing faces across the gender divide. The process was akin to an AI “makeover”: feed in a photo of a woman, and out emerges a version with masculine features; feed in a man, and the network feminizes his appearance. What makes this undertaking compelling are the cultural and representational questions it raises. The AI was not explicitly instructed on what “male” or “female” means, it deduced patterns from its training data, encapsulating prevalent visual stereotypes. The resulting portraits mirror societal biases as the machine subtly adjusts features - smoothing or lightening skin, altering jawlines, and modifying hair or stubble - to produce transformations that are startling and often off-kilter. A face may emerge with features that almost pass as plausibly male or female, yet remain ambiguous with mismatched eyes or an eerie smile. Klingemann embraced these unexpected outcomes as artistic serendipity, applying his transhancement technique to magnify odd details and produce ghostly, uncanny portraits that flicker between genders. These images carry a haunting, archival quality. By mining open archives of 19th-century photography to compile a dataset of vintage faces, Klingemann allowed modern AI to “repaint” anonymous figures of the past. Conceptually, the series engages with themes of fluid identity and the algorithmic gaze. In an era when algorithms increasingly mediate self-presentation, his work questions what it means when a machine reconstructs you according to its learned biases. CycleGAN Makeover stands as a fascinating study in machine bias and creativity, a visual exploration that blurs the line between male and female, provoking us to reconsider our preconceptions.
