Recent advances in complementary label learning

Abstract

Complementary Label Learning (CLL), a crucial aspect of weakly supervised learning, has seen significant theoretical and practical advancements. However, a comprehensive review of the field has been lacking. This survey provides the first exhaustive compilation and synthesis of state-of-the-art CLL approaches, filling a critical gap in the literature and serving as a foundational resource for the community. Key contributions of this survey include an extensive categorization of CLL methodologies, clarifying diverse algorithms based on their principles and applications. This classification scheme enhances understanding of the CLL landscape, highlighting its versatility across varied settings. Additionally, the survey examines the evolution of CLL, showcasing its adaptability and potential in addressing complex applications. It also explores experimental frameworks, including processes for generating complementary labels and datasets and numerical evaluation of existing state-of-the-art. Moreover, the survey delves into how CLL integrates with and enhances other weakly supervised and semi-supervised learning approaches, deepening understanding of its role in the broader machine learning ecosystem. Overall, this survey not only compiles CLL research but also guides future explorations, steering the field towards new horizons in weakly supervised learning.