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

Understand

Generating high-resolution, photo-realistic images has been a long-standing goal in machine learning.

• Recently, Nguyen et al.
• (2016) showed one interesting way to synthesize novel images by performing gradient ascent in the latent space of a generator network to maximize the activations of one or multiple neurons in a separate classifier network.
• In this paper we extend this method by introducing an additional prior on the latent code, improving both sample quality and sample diversity, leading to a state-of-the-art generative model that produces high quality images at higher resolutions (227x227) than previous generative models, and does so for all 1000 ImageNet categories.