Inspirational Adversarial Image Generation
Baptiste Rozi\`ere, Morgane Riviere, Olivier Teytaud, J\'er\'emy, Rapin, Yann LeCun, Camille Couprie

TL;DR
This paper introduces a simple optimization-based method to generate inspiring images from datasets using generative models, allowing user control and preference-based refinement for creative applications.
Contribution
It proposes an optimization approach to find latent space parameters for generating images close to inspirational inputs, enabling user-guided creative image synthesis.
Findings
Effective retrieval of satisfactory images across multiple datasets
Gradient-free optimizers can operate with human preferences without numerical criteria
High-resolution images achieved through progressive GAN training
Abstract
The task of image generation started to receive some attention from artists and designers to inspire them in new creations. However, exploiting the results of deep generative models such as Generative Adversarial Networks can be long and tedious given the lack of existing tools. In this work, we propose a simple strategy to inspire creators with new generations learned from a dataset of their choice, while providing some control on them. We design a simple optimization method to find the optimal latent parameters corresponding to the closest generation to any input inspirational image. Specifically, we allow the generation given an inspirational image of the user choice by performing several optimization steps to recover optimal parameters from the model's latent space. We tested several exploration methods starting with classic gradient descents to gradient-free optimizers. Many…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Aesthetic Perception and Analysis · Face recognition and analysis
