Fusion Architecture

Fusion architecture in deep learning focuses on integrating data from multiple modalities (e.g., images, text, audio) to improve the performance of machine learning models for various tasks. Current research emphasizes developing efficient fusion methods, including early and late fusion strategies, and exploring novel architectures like transformers and convolutional neural networks, often incorporating attention mechanisms to weigh the importance of different modalities. This approach is proving highly effective across diverse applications, from medical diagnosis (e.g., detecting Parkinson's disease or identifying plant species) to security (e.g., deepfake detection) and autonomous driving, leading to more robust and accurate models than those relying on single data sources.