BAyesian Optimal Condensation Framework

Bayesian Optimal Condensation frameworks aim to create significantly smaller, representative subsets of large datasets or neural networks, preserving performance while reducing computational costs and storage needs. Current research focuses on developing efficient algorithms, often employing Bayesian methods or generative models, to condense data into more compact formats like generative models or smaller subnetworks, improving upon previous pixel-based approaches. This work is crucial for advancing machine learning applications where data size is a major bottleneck, enabling faster training, reduced resource consumption, and facilitating the application of deep learning to resource-constrained environments.