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About this paper
Hemanth Chandravamsi, Dhanush V. Shenoy, Steven H. Frankel
Researchers from Technion - Israel Institute of Technology developed WINNER, a weight initialization scheme that enhances Sinusoidal Representation Networks (SIRENs) by adaptively adding Gaussian noise to initial layer weights, achieving state-of-the-art accuracy in audio, image, and 3D signal reconstruction and denoising without increasing model parameters.
Key insights
- Targeted perturbation of initial network weights with noise can effectively expand the frequency support of Implicit Neural Representations.
- The spectral bottleneck in SIRENs is a critical limitation where the network's intrinsic frequency bias severely misaligns with high-frequency target signals.
- Adaptive, target-aware initialization schemes can prime networks to learn specific signal characteristics more efficiently and robustly without adding architectural complexity.
Summary by alphaXiv.
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