Implicitly defined, continuous signal representations parameterized by neural networks have emerged as a powerful paradigm, offering many possible benefits over conventional signal representations. The goal of this project is to understand the effect of various neural network regularization techniques (weight decay, dropout) on the signal representation, and to use that understanding to find optimal regularization strategies for enhancing the denoising capabilities of implicit neural representations. This project requires good PyTorch knowledge.