# Guided Image Generation with Conditional Invertible Neural Networks Source: [Guided Image Generation with Conditional Invertible Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:40:30.056Z Authors: Lynton Ardizzone, Carsten L\\"uth, Jakob Kruse, Carsten Rother, Ullrich K\\"othe Published: 2019-07-04T13:20:57.000Z ## Abstract In this work, we address the task of natural image generation guided by a conditioning input. We introduce a new architecture called conditional invertible neural network (cINN). The cINN combines the purely generative INN model with an unconstrained feed-forward network, which efficiently preprocesses the conditioning input into useful features. All parameters of the cINN are jointly optimized with a stable, maximum likelihood-based training procedure. By construction, the cINN does not experience mode collapse and generates diverse samples, in contrast to e.g. cGANs. At the same time our model produces sharp images since no reconstruction loss is required, in contrast to e.g. VAEs. We demonstrate these properties for the tasks of MNIST digit generation and image colorization. Furthermore, we take advantage of our bi-directional cINN architecture to explore and manipulate emergent properties of the latent space, such as changing the image style in an intuitive way. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Colorization on ImageNet (val)](): Image Colorization; ImageNet (val); FID: 25.13. ## Original sources and implementation [Original paper]() [PDF]()