FiRe: Frequency Reparameterization as a Preconditioner for Periodic Implicit Neural Representations
Periodic Implicit Neural Representations (INRs) such as SIREN and FINER assign every neuron, the same global frequency, spending the representational budget inefficiently when local signal content varies. We introduce FiRe (Frequency Reparameterization), that accelerates optimization by reparameterizing per-neuron frequency of periodic INRs without changing their underlying activation function. FiRe gives each neuron a bounded, input-dependent frequency via a separate low-rank gating path and is applicable to any periodic activation function. The gate acts as an implicit preconditioner that improves optimization conditioning at initialization via the Neural Tangent Kernel (NTK). This bett...
| Authors | Harinandan Shukla, Rajarshi Verma, Jitin Singla |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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