Kohki Akiba

LEVERAGING LABEL PROPORTION PRIOR FOR CLASS-IMBALANCED SEMI-SUPERVISED LEARNING

Semi-supervised learning (SSL) often suffers under class imbalance, where pseudo-labeling amplifies majority bias and suppresses minority performance. We address this issue with a lightweight framework that, to our knowledge, is the first to introduce Proportion Loss from learning from label proport…