Expanding Active Learning Strategies to Multi-Modal Data for Enhanced Cross-Domain Performance
Niyi Favour, olaoye godwin · 2023
In the rapidly evolving field of machine learning, the ability to adapt and generalize across diverse data modalities is essential. Multi-modal data sources, encompassing text, images, audio, and more, offer a wealth of information. However, optimizing performance across these diverse domains is a complex challenge. Active learning techniques, traditionally applied to single-modal data, are now being extended to multi-modal scenarios to unlock their potential for enhancing cross-domain performance. In this article, we explore the concept of multi-modal active learning and its applications in various domains.