Conditional Consistency Regularization for Semi-supervised Multi-label Classification

Abstract

In practical scenarios, a sample may have multiple labels that reveal its classes instead of a single label, which is widely known as multi-label classification (MLC). However, some practical situations may lack reliable labels due to the high cost, time-consuming and professional labelling process. Although Semi-supervised classification may become a potential solution, most of the outstanding existing methods are customized for the single-label situation and ignore multi-label situations. Consistency regularization has performed great success in Weakly/Semi-supervised Single-label classification (SS-SLC), but few efforts have been devoted to semi-supervised Multi-label classification (SS-MLC). A simple solution for introducing consistency regularization to SS-MLC is to regularize predictions of models to be consistent with different augmentation of the same image. Nonetheless, the solution lacks attention to label relations which are crucial components of Multi-label classification.

In the thesis, I go beyond the consistency regularization in SS-SLC and propose Conditional Consistency Regularization (CCR) that is designed for SS-MLC. To be specific, we make potential labels (grand-truth label for labeled samples, pseudo-label for unlabeled samples) conditioned on different label states (i.e., positive, negative, or unknown for each class). By regularizing the two predictions to be invariant, the model can learn label relations implicitly between two different label states, which can boost classification performance. The comprehensive experiments that are conducted on different datasets show that the proposed method can surpass state-of-art SS-MLC and MLC methods by a large gap.