The cyclical learning rate scheme, while effective in capturing multi-modal weight posterior distributions, may have limitations in certain scenarios. One potential limitation is the risk of instability during training, especially when the learning rate is adjusted abruptly between cycles. This can lead to oscillations in the weight space and hinder the convergence of the model. To address this limitation, the scheme can be further improved by incorporating adaptive learning rate strategies that dynamically adjust the learning rate based on the model's performance and convergence rate.
Another limitation of the cyclical learning rate scheme is the potential for local optima trapping, where the model gets stuck in suboptimal solutions within each cycle. To mitigate this issue, researchers can explore hybrid learning rate schedules that combine cyclical learning rates with annealing techniques or momentum adjustments. By fine-tuning the parameters of the cyclical learning rate scheme and experimenting with different cycle lengths and restart strategies, the method can be optimized to effectively capture diverse weight modes and improve the robustness of the uncertainty estimation.