The geometry of efficient codes: how rate-distortion trade-offs distort the latent representations of generative models
<|reference_start|>The geometry of efficient codes: how rate-distortion trade-offs distort the latent representations of generative models: Living organisms rely on internal models of the world to act adaptively. These models cannot encode every detail and hence need to compress information. From a cognitive standpoint, information compression can manifest as a distortion of latent representations, resulting in the emergence of representations that may not accurately reflect the external world or its geometry. Rate-distortion theory formalizes the optimal way to compress information, by considering factors such as capacity limitations, the frequency and the utility of stimuli. However, while this theory explains why the above factors distort latent representations, it does not specify which specific distortions they produce. To address this question, here we systematically explore the geometry of the latent representations that emerge in generative models that operate under the principles of rate-distortion theory ($\beta$-VAEs). Our results highlight that three main classes of distortions of internal representations -- prototypization, specialization, orthogonalization -- emerge as signatures of information compression, under constraints on capacity, data distributions and tasks. These distortions can coexist, giving rise to a rich landscape of latent spaces, whose geometry could differ significantly across generative models subject to different constraints. Our findings contribute to explain how the normative constraints of rate-distortion theory distort the geometry of latent representations of generative models of artificial systems and living organisms.<|reference_end|>
@article{d'amato2024the,
    title={The geometry of efficient codes: how rate-distortion trade-offs distort
  the latent representations of generative models},
    author={Leo D'Amato, Gian Luca Lancia and Giovanni Pezzulo},
    journal={arXiv preprint arXiv:2406.07269},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.07269},
    primaryClass={q-bio.NC cs.IT math.IT}
}