Dataset Generation

Dataset generation focuses on automatically creating large, high-quality datasets for training machine learning models, addressing the limitations of manually curated data. Current research emphasizes using generative models, such as diffusion models and GANs, often coupled with LLMs for automated annotation and data augmentation, to create diverse and realistic synthetic datasets for various tasks including image classification, object detection, and natural language processing. This field is crucial for advancing machine learning research by providing access to large, labeled datasets for diverse applications, particularly in areas where real-world data is scarce, expensive, or ethically problematic.