Discovers that pretraining Implicit Neural Representations (INRs) on structured $1/f^\alpha$ noise performs as well as data-driven initialization.
April 1, 2026
Original Paper
The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations
arXiv · 2603.29034
The Takeaway
It shows that the benefits of data-driven initialization for INRs can be replicated by simple statistical noise structures found in nature. This allows practitioners to achieve high-performance INR training in domains where pre-existing datasets are unavailable or expensive.
From the abstract
The approximation and convergence properties of implicit neural representations (INRs) are known to be highly sensitive to parameter initialization strategies. While several data-driven initialization methods demonstrate significant improvements over standard random sampling, the reasons for their success -- specifically, whether they encode classical statistical signal priors or more complex features -- remain poorly understood. In this study, we explore this phenomenon through a series of expe