Unified Complementary Learning with Feature Perturbation for Semi-Supervised Learning

Semi-supervised learning has attracted widespread attention due to its ability to utilize both labeled and unlabeled data, leading to significant progress in recent years. Conventional semi-supervised learning approaches often rely on a strategy that combines weak and strong image-level augmentations and employs pseudo-labeling techniques, where high-confidence predictions are selected as pseudo-labels while low-confidence ones are discarded. However, such methods tend to overlook the useful information contained in low-confidence samples. Moreover, existing augmentation strategies are mostly limited to the image level and lack feature-level perturbations. To address these limitations, this paper proposes a semi-supervised learning method that integrates complementary labels and auxiliary feature perturbations, aiming to extract valuable information from low-confidence samples and expand the scope of feature-space perturbations. Experiments on the standard CIFAR-10 dataset show that under the extreme setting of only 4 labels per class, our method improves accuracy by 2.58% compared to Fixmatch. On the more complex STL-10 dataset, also with only 4 labels per class, the Top-1 accuracy is improved by 2.59%. In addition, systematic ablation studies are conducted to verify the effectiveness of each component.