Sample Amplification

Sample amplification explores methods for artificially increasing the size of a dataset while maintaining its statistical properties, aiming to improve the performance of machine learning models or enhance the analysis of scientific data. Current research focuses on developing algorithms, including machine learning models, to generate synthetic data points that are indistinguishable from real samples, addressing challenges like vanishing gradients in deep learning and mitigating biases in datasets. This field is significant because it can improve model training efficiency, enhance robustness against adversarial attacks, and help address data scarcity issues in various scientific domains, including health research and image analysis.