Description: Adaptive Batch Size is a technique used in the training of machine learning models, including convolutional neural networks, that dynamically adjusts the batch size of data processed in each iteration. This strategy aims to optimize model performance and resource utilization, allowing the algorithm to adapt to the characteristics of the dataset and the available hardware capacity. By varying the batch size, a balance can be struck between training speed and model accuracy, as a smaller batch size may provide better generalization, while a larger batch size can accelerate the training process. This technique is particularly useful in situations where resources are limited or when working with large datasets, as it allows for more efficient use of memory and processing power. Additionally, Adaptive Batch Size can help avoid issues such as overfitting by introducing variability into the training process, which can lead to better model convergence. In summary, this technique is a valuable tool for researchers and developers looking to enhance the performance of their machine learning models, adapting to the changing conditions of the training environment.
