Training Instance

Training instance selection and manipulation are crucial for improving the efficiency and robustness of machine learning models, particularly in resource-constrained scenarios like few-shot learning and extreme multi-label classification. Current research focuses on developing efficient algorithms for selecting representative subsets of training data, leveraging model training dynamics for improved fine-tuning, and incorporating human feedback to enhance model performance. These advancements are significant because they address challenges related to computational cost, data imbalance, and the need for more adaptable and robust models across various applications, including natural language processing and image captioning.