Wave-based cross-phase representation for weakly supervised classification

Abstract

Weakly Supervised Learning (WSL) aims to improve model robustness and manage label uncertainty, but current methods struggle to handle various weak label sources, such as incomplete and noisy labels. Additionally, these methods struggle with a lack of adaptability from reliance on prior knowledge and the complexity of managing data-label dependencies. To address these problems, we propose a wave-based cross-phase network (WCPN) to enhance adaptability for incomplete and noisy labels. Specifically, we expand wave representations and design a cross-phase token mixing (CPTM) module to refine feature relationships and integrate strategies for various weak labels. The proposed CPFE algorithm in the CPTM optimizes feature relationships by using self-interference and mutual-interference to process phase information between feature tokens, thus enhancing semantic consistency and discriminative ability. Furthermore, by employing a data-driven tri-branch structure and maximizing mutual information between features and labels, WCPN effectively overcomes the inflexibility caused by reliance on prior knowledge and complex data-label dependencies. In this way, WCPN leverages wave representations to enhance feature interactions, capture data complexity and diversity, and improve feature compactness for specific categories. Experimental results demonstrate that WCPN excels across various supervision levels and consistently outperforms existing advanced methods. It effectively handles noisy and incomplete labels, showing remarkable adaptability and enhanced feature understanding.