Hybrid Data

Hybrid data, combining diverse data sources like real and synthetic images, text and tables, or frame-based and event-based sensor data, is a rapidly growing area of research aimed at improving the robustness and performance of machine learning models. Current research focuses on leveraging hybrid datasets to train models for various tasks, including object detection, image editing, question answering, and deepfake detection, often employing deep learning architectures like convolutional neural networks and large language models, along with techniques like federated learning and data augmentation. This approach addresses limitations of using single data types, leading to more accurate and efficient models with broader applicability across diverse real-world scenarios.