Dataset Pruning

Dataset pruning aims to reduce the size of training datasets for machine learning models without significantly sacrificing performance, thereby lowering computational costs and resource demands. Current research focuses on developing efficient algorithms that select the most informative data points, often leveraging model performance metrics, training dynamics, or even incorporating concepts like memory and fairness considerations across various architectures including transformers, convolutional neural networks, and neural radiance fields. This area is significant because it addresses the scalability challenges posed by ever-growing datasets, enabling more efficient training of complex models and potentially democratizing access to advanced machine learning techniques.