Diva 360

DiVA (in its various forms) represents a family of algorithms and datasets addressing diverse challenges in machine learning. Current research focuses on improving efficiency and accuracy in tasks such as document-level relation extraction, federated learning for image classification, and dynamic neural field modeling, often leveraging deep learning architectures and variational autoencoders. These advancements aim to enhance the performance and robustness of machine learning models across various applications, from information extraction and music labeling to privacy-preserving training and high-fidelity 3D scene capture. The development of datasets like DiVa-360 is crucial for benchmarking and advancing these methods.