While the confidence-based stopping criterion offers a promising approach for early stopping in resonator networks, there are potential limitations that could be addressed to further improve its effectiveness. One limitation is the sensitivity of the confidence metric to the specific threshold values used for determining convergence. To mitigate this, a more adaptive thresholding strategy could be implemented, such as dynamically adjusting the threshold based on the convergence behavior of the network during training.
Another limitation is the reliance on inner product values between the estimated factors and the codebook vectors to compute confidence. This approach may not capture the full complexity of the factorization problem, especially in cases where the resonator network converges to suboptimal solutions. To enhance the confidence metric, additional measures of similarity or discrepancy between the estimated factors and the ground truth could be incorporated, providing a more comprehensive assessment of convergence.
Furthermore, combining the confidence-based stopping criterion with other convergence criteria, such as monitoring changes in the network's output stability or error rate, could offer a more robust approach to determining convergence. By integrating multiple stopping criteria, the system can make more informed decisions about when to halt the factorization process, improving efficiency and accuracy.