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Generating high-resolution, photo-realistic images has been a long-standinggoal in machine learning. Recently, Nguyen et al. (2016) showed one interestingway to synthesize novel images by performing gradient ascent in the latentspace of a generator network to maximize the activations of one or multipleneurons in a separate classifier network. In this paper we extend this methodby introducing an additional prior on the latent code, improving both samplequality and sample diversity, leading to a state-of-the-art generative modelthat produces high quality images at higher resolutions (227x227) than previousgenerative models, and does so for all 1000 ImageNet categories. In addition,we provide a unified probabilistic interpretation of related activationmaximization methods and call the general class of models "Plug and PlayGenerative Networks". PPGNs are composed of 1) a generator network G that iscapable of drawing a wide range of image types and 2) a replaceable "condition"network C that tells the generator what to draw. We demonstrate the generationof images conditioned on a class (when C is an ImageNet or MIT Placesclassification network) and also conditioned on a caption (when C is an imagecaptioning network). Our method also improves the state of the art ofMultifaceted Feature Visualization, which generates the set of synthetic inputsthat activate a neuron in order to better understand how deep neural networksoperate. Finally, we show that our model performs reasonably well at the taskof image inpainting. While image models are used in this paper, the approach ismodality-agnostic and can be applied to many types of data.

Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space