Sampling Control for Imbalanced Calibration in Semi-Supervised Learning

TL;DR

This paper introduces SC-SSL, a novel sampling control framework that effectively addresses class imbalance and distribution mismatch in semi-supervised learning, achieving state-of-the-art results.

Contribution

The paper proposes a unified, decoupled sampling control method for semi-supervised learning to better handle class imbalance and distribution mismatch.

Findings

SC-SSL outperforms existing methods on benchmark datasets.

It effectively mitigates feature-level imbalance for minority classes.

Achieves state-of-the-art performance across various distribution settings.

Abstract

Class imbalance remains a critical challenge in semi-supervised learning (SSL), especially when distributional mismatches between labeled and unlabeled data lead to biased classification. Although existing methods address this issue by adjusting logits based on the estimated class distribution of unlabeled data, they often handle model imbalance in a coarse-grained manner, conflating data imbalance with bias arising from varying class-specific learning difficulties. To address this issue, we propose a unified framework, SC-SSL, which suppresses model bias through decoupled sampling control. During training, we identify the key variables for sampling control under ideal conditions. By introducing a classifier with explicit expansion capability and adaptively adjusting sampling probabilities across different data distributions, SC-SSL mitigates feature-level imbalance for minority classes. In the inference phase, we further analyze the weight imbalance of the linear classifier and apply post-hoc sampling control with an optimization bias vector to directly calibrate the logits. Extensive experiments across various benchmark datasets and distribution settings validate the consistency and state-of-the-art performance of SC-SSL.