Target Dataset

Target datasets are crucial for training and evaluating machine learning models, particularly in scenarios with limited data or significant domain shifts. Current research focuses on improving dataset quality through careful annotation strategies, addressing data imbalance issues, and exploring domain adaptation techniques to leverage auxiliary datasets for better model generalization. This work is vital for advancing various applications, including object detection in remote sensing, reinforcement learning, and handwriting recognition, by ensuring robust and reliable model performance in real-world settings. The development of high-quality benchmark datasets and efficient methods for data collection and curation are key areas of ongoing investigation.