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Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations
Michael Poli · Winnie Xu · Stefano Massaroli · Chenlin Meng · Kuno Kim · Stefano Ermon

Wed Nov 30 09:00 AM -- 11:00 AM (PST) @ Hall J #431

Many patterns in nature exhibit self-similarity: they can be compactly described via self-referential transformations. Said patterns commonly appear in natural and artificial objects, such as molecules, shorelines, galaxies, and even images. In this work, we investigate the role of learning in the automated discovery of self-similarity and in its utilization for downstream tasks. To this end, we design a novel class of implicit operators, Neural Collages, which (1) represent data as the parameters of a self-referential, structured transformation, and (2) employ hypernetworks to amortize the cost of finding these parameters to a single forward pass. We detail how to leverage the representations produced by Neural Collages in various tasks, including data compression and generation. Neural Collage image compressors are orders of magnitude faster than other self-similarity-based algorithms during encoding and offer compression rates competitive with implicit methods. Finally, we showcase applications of Neural Collages for fractal art and as deep generative models.

Author Information

Michael Poli (Stanford University)
Winnie Xu (University of Toronto / Stanford University)
Stefano Massaroli (Mila - Quebec AI Institute)
Chenlin Meng (Stanford University)
Kuno Kim (Stanford)
Stefano Ermon (Stanford)

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