Fusion Based Deep Learning

Fusion-based deep learning integrates data from multiple sources (e.g., audio, video, sensor readings, medical images) to improve the accuracy and robustness of machine learning models. Current research emphasizes various fusion strategies (early, intermediate, late, hybrid) implemented within diverse architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and even combinations of analog and spiking neural networks. This approach is proving valuable across numerous applications, from improving medical diagnoses (e.g., Parkinson's detection) and enhancing security systems (e.g., violence detection) to advancing autonomous vehicle perception and remote sensing image analysis. The ability to leverage complementary information from multiple modalities is driving significant advancements in various fields.