Algorithms that get old : the case of generative algorithms
- URL: http://arxiv.org/abs/2202.03008v1
- Date: Mon, 7 Feb 2022 08:55:37 GMT
- Title: Algorithms that get old : the case of generative algorithms
- Authors: Gabriel Turinici
- Abstract summary: Generative IA networks produce new objects each time when asked to do so.
This behavior is unlike that of human artists that change their style as times go by and seldom return to the initial point.
We propose a numerical paradigm, to be used in conjunction with a generative algorithm, that satisfies the two following requirements: the objects created do not repeat and evolve to fill the entire target probability measure.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Generative IA networks, like the Variational Auto-Encoders (VAE), and
Generative Adversarial Networks (GANs) produce new objects each time when asked
to do so. However, this behavior is unlike that of human artists that change
their style as times go by and seldom return to the initial point. We
investigate a situation where VAEs are requested to sample from a probability
measure described by some empirical set. Based on recent works on Radon-Sobolev
statistical distances, we propose a numerical paradigm, to be used in
conjunction with a generative algorithm, that satisfies the two following
requirements: the objects created do not repeat and evolve to fill the entire
target probability measure.
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