COIN (2021), credited by its successors as the first INR-based image-compression method, does not cite fractal, IFS, or WFA compression and traces its own lineage to Stanley's 2007 CPPN paper instead

implicit-neural-representationscoinimage-compressionfractal-compressionnegative-citationargument-from-silencehistory-of-machine-learning

Dupont et al.'s "COIN: COmpression with Implicit Neural representations" — credited by its own successors as the first paper to use implicit neural representations for image compression, a priority COIN never claims for itself — was read in full, including its complete 40-entry reference list. Neither "fractal," "Barnsley," "Jacquin," nor any form of "weighted finite automata" appears anywhere in the text.

COIN's own "Related Work" section credits a different origin point entirely: "Representing data with neural networks was originally proposed by [32] but has seen a recent surge in interest in the 3D vision community [27, 26, 6]." Reference [32] is Kenneth O. Stanley's 2007 paper on compositional pattern-producing networks (CPPNs) — COIN's own choice of ancestor for "representing data with neural networks." This is notable because COIN is, unlike SIREN, a compression paper specifically — the direct point of comparison to classical fractal/IFS image compression — and it still does not engage that literature.

Paired with claim-siren-2020-does-not-cite-fractal-ifs-wfa-compression (same absence in the field's foundational paper) and claim-strumpler-2022-omits-fractal-compression-credits-coin-as-first (COIN's own direct successor, same absence, and which in turn names COIN "the first" INR-compression approach — a claim made without reference to the older compression tradition). Contrasts with claim-poli-et-al-2022-neural-collages-bridges-fractal-and-inr-canons, the one paper found to draw the fractal↔INR line explicitly, which does cite COIN by name from the fractal-compression side. See also moc-argument-from-silence.