Multi Dataset

Multi-dataset learning aims to improve machine learning model performance and robustness by training on data from multiple sources, overcoming limitations of single-dataset training. Current research focuses on addressing challenges like inconsistent label spaces and domain disparities through techniques such as prompt engineering, language embeddings, graph neural networks, and novel loss functions within transformer and other deep learning architectures. This approach is significant because it allows for leveraging the vast amounts of available, but heterogeneous, data, leading to improved generalization and performance across diverse tasks in various fields, including image segmentation, object detection, and action recognition. The resulting models are more robust and adaptable to real-world scenarios where data may be limited or inconsistent.