Cyclic Training

Cyclic training is a machine learning technique that involves systematically varying training parameters or data characteristics over multiple cycles, aiming to improve model performance and robustness. Current research explores cyclic variations in diverse aspects, such as weight decay, batch size, loss functions, and data augmentation strategies, often applied to convolutional neural networks (CNNs) and singing voice synthesis models. This approach shows promise in addressing challenges like quantization errors in low-bit CNNs and data scarcity in generative models, leading to improved accuracy and efficiency in various applications. The broader impact lies in providing a flexible framework for optimizing the training process across different neural network architectures and datasets.