Neural Abstractions (2017) by Mario Klingemann

Neural Abstractions represents Klingemann’s breakthrough into true AI-assisted image generation, using early deep neural networks to summon surreal new visions. Created in 2016–17, these works grew out of experiments with Plug-and-Play Generative Networks (PPGN), an innovative technique that predates GANs, allowing an AI to generate images by maximizing the outputs of a classifier rather than by direct synthesis. Instead of training a network to draw per se, Klingemann ingeniously trained a suite of custom classifier models (on datasets he curated himself) and then set a generative process to “please” those models. In practice, he fed the network a target concept – not via text prompt, but via a classifier node such as “portrait” or “map”, and the system iteratively adjusted a noise image until the classifier was highly activated. Minted in 2024-2025