The delta-bar-delta algorithm is recognized as a learning rate adaptation technique that enhances the convergence speed of the training process in optimization by dynamically scheduling the learning rate based on the difference between the current and previous weight updates. While this algorithm has demonstrated strong competitiveness in full data optimization when compared to other state-of-the-art algorithms like Adam and SGD, it may encounter convergence issues in mini-batch optimization scenarios due to the presence of noisy gradients. In this study, we thoroughly investigate the convergence behavior of the delta-bar-delta algorithm in real-world neural network optimization. To address any potential convergence challenges, we propose a novel approach called RDBD (Regrettable Delta-Bar-Delta). Our approach allows for prompt correction of biased learning rate adjustments and ensures the convergence of the optimization process. Furthermore, we demonstrate that RDBD can be seamlessly integrated with any optimization algorithm and significantly improve the convergence speed. By conducting extensive experiments and evaluations, we validate the effectiveness and efficiency of our proposed RDBD approach. The results showcase its capability to overcome convergence issues in mini-batch optimization and its potential to enhance the convergence speed of various optimization algorithms. This research contributes to the advancement of optimization techniques in neural network training, providing practitioners with a reliable automatic learning rate scheduler for achieving faster convergence and improved optimization outcomes.

翻译：delta-bar-delta算法是一种学习率自适应技术，通过基于当前与先前权重更新的差值动态调度学习率，提升优化训练过程的收敛速度。尽管该算法在全数据优化中与Adam、SGD等先进算法相比展现出强劲竞争力，但在小批量优化场景下可能因梯度噪声而遭遇收敛问题。本研究深入探究了delta-bar-delta算法在实际神经网络优化中的收敛行为，并提出了一种名为RDBD（Regrettable Delta-Bar-Delta）的新型方法以应对潜在收敛挑战。该方法能够及时修正有偏的学习率调整，确保优化过程的收敛性。此外，我们证实RDBD可与任意优化算法无缝集成并显著提升收敛速度。通过大量实验与评估，我们验证了所提RDBD方法的有效性与高效性。结果表明，该方法能克服小批量优化中的收敛问题，并具备提升多种优化算法收敛速度的潜力。本研究推动了神经网络训练优化技术的发展，为实践者提供了一种可靠的自动学习率调度工具，助力实现更快收敛与更优的优化结果。