Representation Fusion

Representation fusion aims to combine information from multiple sources (e.g., images, text, sensor data) to create more comprehensive and robust representations for various machine learning tasks. Current research focuses on developing efficient fusion techniques, including attention mechanisms, transformer networks, and autoencoders, often tailored to specific data modalities and addressing challenges like data heterogeneity and missing views. These advancements are improving performance in diverse applications such as image classification, natural language processing, and healthcare monitoring, leading to more accurate and reliable models.