Training Dataset Size

Research on training dataset size investigates how the quantity of data impacts machine learning model performance, focusing on optimizing data efficiency and predicting necessary sample sizes for achieving target accuracy. Current studies explore this across various model architectures, including end-to-end learning and local feature methods for image recognition, and transformer-based language models, analyzing the effects of both overall dataset size and class-specific data imbalances. Understanding these relationships is crucial for improving model development, reducing annotation costs, and enabling effective machine learning in resource-constrained scenarios, particularly in fields like wildlife monitoring and computational social science.