Learning Rate Scheduler

Learning rate schedulers dynamically adjust the learning rate during training of neural networks, aiming to improve training efficiency and model performance. Recent research focuses on developing schedulers that require minimal hyperparameter tuning, such as those based on locally optimal descent or leveraging the asymptotic behavior of hyperbolic functions to achieve consistent performance across varying training epochs. These advancements address the challenges of manual tuning and inconsistent learning curves observed in traditional methods, leading to more robust and efficient training of diverse neural network architectures, including generative adversarial networks (GANs). The resulting improvements in training stability and model quality have significant implications for various applications, from image generation to domain adaptation.