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<p>By sampling from the latent space of an autoencoder and decoding the latent space samples to the original data space, any autoencoder can be turned into a generative model. For this to work, it is necessary to model the latent space with a distribution from which samples can be obtained. Several simple possibilities such as kernel density estimates or a Gaussian distribution and more sophisticated ones such as Gaussian mixture models, copula models, and normalization flows can be thought of and have been tried recently. In a plain-vanilla autoencoder setting, this study aims to discuss, assess, and compare various techniques that can be used to capture the latent space so that an autoencoder can become a generative model. Furthermore, we provide insights into further aspects of these methods, such as targeted sampling or synthesizing new data with specific features.</p>

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A comparison of latent space modeling techniques in a plain-vanilla autoencoder setting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月23日

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  • Fabian Kächele,
  • Maximilian Coblenz,
  • Oliver Grothe

摘要

By sampling from the latent space of an autoencoder and decoding the latent space samples to the original data space, any autoencoder can be turned into a generative model. For this to work, it is necessary to model the latent space with a distribution from which samples can be obtained. Several simple possibilities such as kernel density estimates or a Gaussian distribution and more sophisticated ones such as Gaussian mixture models, copula models, and normalization flows can be thought of and have been tried recently. In a plain-vanilla autoencoder setting, this study aims to discuss, assess, and compare various techniques that can be used to capture the latent space so that an autoencoder can become a generative model. Furthermore, we provide insights into further aspects of these methods, such as targeted sampling or synthesizing new data with specific features.