Closing the Capacity–Convergence Gap: Globally Optimal Configuration of Implicit Neural Representations

Implicit Neural Representations (INRs) achieve state-of-theart results across image representation, 3D modeling, and novel-view synthesis, yet their performance hinges on tightly coupled design decisions— activation family, initialization scheme, and per-layer hyperparameters— whose interdependencies render manual tuning unreliable and greedy search provably suboptimal. We introduce OptiINR, the first framework that recasts INR configuration as a global optimization problem over a mixed-variable space of discrete activation choices (e.g., SIREN, WIRE, FINER) and their continuous parameters. OptiINR consists of a structured layer-wise INR configuration space and an adaptive Bayesian optimization procedure for navigating it. The search space spans activation families, activation-specific continuous parameters, initialization/scaling choices, per-layer learning rates, and positional-encoding usage. Across five tasks—1D audio, 2D image, 3D occupancy, spatiotemporal PDE, and neural radiance field synthesis—OptiINR consistently outperforms handtuned baselines and prior automated methods, revealing task-dependent activation patterns that challenge prevailing one-size-fits-all design conventions. Code is available at https://github.com/Sippengg/OptiINR.