Synthetic Spectrum

Synthetic spectrum research focuses on generating artificial spectral data to address limitations in real-world datasets, such as scarcity, noise, or domain shifts. Current efforts utilize various machine learning architectures, including autoencoders, generative adversarial networks (GANs), and convolutional neural networks (CNNs), often within transfer learning or multi-task learning frameworks, to improve model training and performance in diverse applications. This work is significant because it enables advancements in fields like medical imaging, astronomy, and materials science by providing robust and scalable methods for analyzing spectral data, ultimately leading to more accurate and efficient data analysis.