Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation
We study sinusoidal recurrence as an iterative mechanism for harmonic spectral enrichment in implicit neural representations (INRs). Our analysis reveals that sinusoidal activations induce a harmonic line spectrum, providing a spectral account of how recurrent unrolling enriches the effective spectral support. We realize this principle with a shared sinusoidal block that iteratively refines the latent representation. We empirically validate the resulting spectral behavior against feed-forward INRs, non-sinusoidal recurrent variants, and equilibrium-style sinusoidal models. Complementing this a
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- FuzzyOverlapping authors or contributors · 62%HKUDS/LightRAG →
“Shared author/contributor keys: jin”
- FuzzyOverlapping authors or contributors · 62%keras-team/keras →
“Shared author/contributor keys: jin”
- LinkedLinked via arxiv author · 85%Hyunmin Cho →
“Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation”
- LinkedLinked via arxiv author · 85%Jaejun Yoo →
“Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation”
- LinkedLinked via arxiv author · 85%Kyong Hwan Jin →
“Recurrent Sinusoidal INRs for Efficient High-Fidelity Representation”