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Many-Objective Optimization for Diverse Image Generation

Abstract : In image generation, where diversity is critical, people can express their preferences by choosing among several proposals. Thus, the image generation system can be refined to satisfy the user's needs. In this paper, we focus on multi-objective optimization as a tool for proposing diverse solutions. Multiobjective optimization is the area of research that deals with optimizing several objective functions simultaneously. In particular, it provides numerous solutions corresponding to trade-offs between different objective functions. The goal is to have enough diversity and quality to satisfy the user. However, in computer vision, the choice of objective functions is part of the problem: typically, we have several criteria, and their mixture approximates what we need. We propose a criterion for quantifying the performance in multi-objective optimization based on cross-validation: when optimizing n−1 of the n criteria, the Pareto front should include at least one good solution for the removed n th criterion. After providing evidence for the validity and usefulness of the proposed criterion, we show that the diversity provided by multiobjective optimization is helpful in diverse image generation, namely super-resolution and inspirational generation.
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https://hal.archives-ouvertes.fr/hal-03425742
Contributor : Laurent Najman Connect in order to contact the contributor
Submitted on : Thursday, November 11, 2021 - 11:33:52 AM
Last modification on : Friday, January 14, 2022 - 3:42:13 AM
Long-term archiving on: : Saturday, February 12, 2022 - 6:12:15 PM

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  • HAL Id : hal-03425742, version 1

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Nathanaël Carraz Rakotonirina, Andry Rasoanaivo, Laurent Najman, Petr Kungurtsev, Jeremy Rapin, et al.. Many-Objective Optimization for Diverse Image Generation. 2021. ⟨hal-03425742⟩

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