Bag Prototype

"Bag" in machine learning research refers to a collection of data points treated as a single unit, often used in weakly supervised learning or to address challenges like data scarcity or high dimensionality. Current research focuses on improving bag-based methods through techniques like label perturbation, fine-tuning large language models for bag representation, and developing efficient algorithms for handling large bags or high-dimensional data, often employing convolutional neural networks, transformers, and factorization machines. These advancements enhance the robustness and efficiency of various machine learning tasks, including image classification, video object segmentation, and topic modeling, leading to improved accuracy and scalability in diverse applications.