The relevance threshold mechanism, while effective in balancing the training dynamics between the generator and discriminator, has potential limitations. One significant limitation is the sensitivity to the choice of the hyperparameter ν, which defines the number of recent epochs considered for calculating the relevant performance score (RPS). If ν is set too low, the mechanism may react too quickly to fluctuations in performance, leading to instability. Conversely, if ν is too high, it may not respond adequately to genuine performance issues, resulting in prolonged periods of mode collapse.
To improve the robustness and stability of the training process, adaptive mechanisms could be introduced. For instance, implementing a dynamic adjustment of ν based on the training progress or the observed variance in RPS could help maintain a balance between responsiveness and stability. Additionally, incorporating a feedback loop that analyzes the historical performance trends of both networks could provide insights into when to adjust the training allocation more effectively.
Another enhancement could involve integrating ensemble methods, where multiple models are trained simultaneously, and their performance metrics are aggregated to determine the training adjustments. This could mitigate the risk of overfitting to specific performance metrics and provide a more comprehensive view of the training dynamics.