Filling Gap

"Filling gaps" encompasses a broad range of research addressing incomplete or missing data across diverse fields. Current efforts focus on developing robust algorithms and models, including neural networks (e.g., variational autoencoders, generative adversarial networks), to effectively reconstruct missing information in various data types (e.g., code, images, point clouds, time series). These advancements are crucial for improving the accuracy and reliability of analyses in numerous applications, from healthcare diagnostics and autonomous systems to natural language processing and 3D reconstruction. The ultimate goal is to create more complete and reliable datasets and models, leading to more accurate and insightful results across scientific disciplines and practical applications.