Grids Often Outperform Implicit Neural Representations

• URL: http://arxiv.org/abs/2506.11139v1
• Date: Tue, 10 Jun 2025 23:52:09 GMT
• Title: Grids Often Outperform Implicit Neural Representations
• Authors: Namhoon Kim, Sara Fridovich-Keil,
• Abstract summary: Implicit Neural Representations (INRs) have recently shown impressive results, but their capacity, implicit biases and scaling behavior remain poorly understood.<n>We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth.<n>We find that, for most tasks, a simple regularized grid with trains faster and to higher quality than any INR with the same number of parameters.
• Score: 5.29241182750977
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit Neural Representations (INRs) have recently shown impressive results, but their fundamental capacity, implicit biases, and scaling behavior remain poorly understood. We investigate the performance of diverse INRs across a suite of 2D and 3D real and synthetic signals with varying effective bandwidth, as well as both overfitting and generalization tasks including tomography, super-resolution, and denoising. By stratifying performance according to model size as well as signal type and bandwidth, our results shed light on how different INR and grid representations allocate their capacity. We find that, for most tasks and signals, a simple regularized grid with interpolation trains faster and to higher quality than any INR with the same number of parameters. We also find limited settings where INRs outperform grids -- namely fitting signals with underlying lower-dimensional structure such as shape contours -- to guide future use of INRs towards the most advantageous applications. Code and synthetic signals used in our analysis are available at https://github.com/voilalab/INR-benchmark.

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