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- W4298181184 abstract "Generative deep neural networks used in machine learning, like the Variational Auto-Encoders (VAE), and Generative Adversarial Networks (GANs) produce new objects each time when asked to do so with the constraint that the new objects remain similar to some list of examples given as input. However, this behavior is unlike that of human artists that change their style as time goes by and seldom return to the style of the initial creations. We investigate a situation where VAEs are used to sample from a probability measure described by some empirical dataset. 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 distribution." @default.
- W4298181184 created "2022-10-01" @default.
- W4298181184 creator A5030131513 @default.
- W4298181184 date "2022-09-19" @default.
- W4298181184 modified "2023-09-27" @default.
- W4298181184 title "Algorithms that get old : the case of generative deep neural networks" @default.
- W4298181184 hasPublicationYear "2022" @default.
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