Data Collection

Data collection, a crucial bottleneck in many machine learning applications, focuses on efficiently acquiring and preparing high-quality datasets for model training and evaluation. Current research emphasizes developing automated and active data acquisition strategies, leveraging techniques like self-supervised and few-shot learning, along with advanced model architectures such as transformers and graph neural networks, to address data scarcity and improve data quality. These advancements are significantly impacting various fields, from medical imaging and robotics to traffic management and environmental monitoring, by enabling the development of more robust and accurate AI systems.