\section{Limitations \& Future Work}
While our proposed approach demonstrates promising results, we identify several limitations and directions for future research.
First, the expressiveness of current MLP-based NFs remains a key bottleneck, as even relatively large models struggle to capture high-dimensional, complex signals. Developing more expressive architectures is therefore a promising research direction, particularly in the largely overlooked context of generalization.
Second, the conditioning mechanism used in this paper incurs a substantial parameter overhead. Future work could explore parameter-sharing strategies or alternative conditioning schemes like signal-specific low-rank adaptations of weights and biases.
Third, we believe that our novel view on SIRENs $\omega$-parameter and its relation to the network's learning dynamics should be further explored. While substantial progress has been made on NF architectures, activations, and initialization, the role of NF learning dynamics is an interesting yet underexplored direction.
Finally, numerous downstream tasks like inpainting, registration, or generation are worth being exploring within our framework. We release the MedNF dataset to support research in this direction, aiming to facilitate the development of efficient, high-performing architectures on our proposed representation and encourage the community to investigate downstream applications.
