Multi Modal Deep Learning

Multi-modal deep learning integrates information from diverse data sources (e.g., images, text, audio) to improve the accuracy and robustness of machine learning models. Current research focuses on developing effective fusion techniques within various architectures, including transformers and autoencoders, to combine these modalities for tasks ranging from medical diagnosis and prognosis to scene recognition and music retrieval. This approach is proving valuable across numerous fields, enhancing prediction accuracy and enabling more comprehensive analyses than single-modality methods, particularly in applications where multiple data types naturally exist. The resulting improvements in model performance have significant implications for various scientific disciplines and practical applications.