While the neural polarization approach in Polar-FFA presents several advantages, it also has potential limitations that warrant consideration. One significant limitation is the reliance on the assumption that the distribution of positive and negative samples is balanced. In real-world applications, this balance may not always hold, leading to suboptimal performance if one class dominates the training data. Future work could address this issue by implementing adaptive mechanisms that dynamically adjust the polarization of neurons based on the distribution of incoming data, ensuring that the model remains effective even in imbalanced scenarios.
Another limitation is the increased complexity introduced by the neural division into positive and negative sets, which may complicate the training process and increase the computational burden. To mitigate this, future research could explore more efficient training algorithms or optimization techniques that reduce the overhead associated with managing two distinct neuron groups. Additionally, the impact of hyperparameter tuning on the performance of Polar-FFA could be further investigated, with the aim of developing automated tuning methods that enhance the model's adaptability across various tasks and datasets.