While implicit neural representations offer flexibility and expressiveness in modeling complex signals, there are several limitations and challenges in applying them to signal processing tasks beyond NMF:
Computational Complexity: Training neural networks for implicit representations can be computationally intensive, especially for high-dimensional signals or large datasets. This complexity can hinder real-time applications or scalability to massive datasets.
Interpretability: Unlike explicit mathematical formulations, implicit neural representations lack direct interpretability. Understanding the learned functions and their impact on the signal processing task can be challenging, making it harder to debug or optimize the models.
Generalization: Implicit representations may struggle with generalizing to unseen data or variations in input signals. Ensuring robustness and adaptability across different signal types or conditions can be a significant challenge.
Data Efficiency: Implicit neural representations often require large amounts of training data to learn meaningful features effectively. Limited data availability or data imbalance can hinder the performance of models based on implicit representations.
Hyperparameter Tuning: Selecting appropriate architectures, activation functions, and optimization strategies for implicit neural networks can be non-trivial. Hyperparameter tuning becomes crucial but time-consuming in optimizing the performance of these models.