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Do generative models learn rare generative factors?
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DOI：10.3389/frai.2025.1697139.png)
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Abstract
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Generative models are becoming a promising tool in AI alongside discriminative learning. Several models have been proposed to learn in an unsupervised fashion the corresponding generative factors; namely the latent variables critical for capturing the full spectrum of data variability. Diffusion Models (DMs); Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are of particular interest due to their impressive ability to generate highly realistic data. Through a systematic empirical study; this paper delves into the intricate challenge of how DMs; GANs and VAEs internalize and replicate rare generative factors. Our findings reveal a pronounced tendency toward memorization of these factors. We study the reasons for this memorization and demonstrate that strategies such as spectral decoupling can mitigate this issue to a certain extent.1
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