Incorporating both channel independence and mixing may have potential drawbacks or limitations depending on the specific application:
Complexity: Combining these two approaches can increase model complexity and computational requirements, making it challenging to scale for large datasets or real-time applications.
Training Costs: Fine-tuning a shared model for each channel while maintaining channel specificity can lead to increased training costs in terms of time and resources.
Robustness vs Performance Trade-off: Balancing between robustness gained from channel independence and performance improvements from channel mixing can be a delicate trade-off that requires careful optimization.
Interpretability: The combined approach might make it harder to interpret how each component contributes to the final prediction due to the intricate interactions between independent and mixed channels.