TL;DR
A novel semi-supervised framework that disentangles pseudo-label generation from the classification task via designing a dedicated pseudo-label generator to align the class distributions between labeled and unlabeled data is proposed.
DPCL was leveraged to learn better representation distributions by uniformly projecting data on a hypersphere, and by which more accurate pseudo labels could be generated, and achieves consistent improvements over existing state-of-the-art methods across multiple benchmarks.
A data-driven semi-supervised framework for imbalanced binary image classification that does not depend on data augmentation, enabling reliable utilization of unlabeled data without introducing augmentation induced noise is introduced.
In Class-Imbalanced Semi-Supervised Learning (CISSL), classifiers suffer from severe confirmation bias, particularly when the class distribution of unlabeled set are unknown or mismatched with that of the labeled set. Existing debiasing methods often rely on impractical assumptions for classifier bias estimation, such as known class priors or risky external proxies. To overcome these limitations, we propose Intrinsic Logit-Based Debiasing (ILBD), a robust post-hoc framework that estimates bias directly from task-relevant data without external dependencies and class priors. ILBD constructs a comprehensive bias estimator by decomposing output logits into two distinct components: (1) Label-Free Intrinsic Bias, derived from non-target K-1 logits by masking target-class maximum logits to capture the model's background distributional skew; and (2) Label-Guided Learning Bias, derived from target-class maximum logits to quantify confidence disparities between head and tail classes arising from varying learning difficulties. To ensure estimation reliability, we further employ a class-adaptive threshold to filter noisy pseudo-labels. By subtracting this estimated bias at inference, ILBD effectively rectifies the classifier's bias directly from the inherent statistical patterns of the training data, without the need for target-prior assumptions or external data. Extensive experiments on CIFAR-10/100-LT, STL-10-LT, and Small-ImageNet-127 demonstrate the effectiveness of ILBD. Our code is available at https://github.com/aroid721/ILBD.
This work proposes a teacher-student semi-supervised learning framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement, and introduces a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments.
LDIBR performs instance-adaptive imputation conditioned on instance features and the binary observation mask, and learns a prior, a reliability-gated correction, and entry-wise fusion weights to produce a normalized imputed distribution.
In semi-supervised object detection (SSOD), due to the limited availability of labeled data, the quality and quantity of pseudo labels generated from unlabeled images are crucial for model training. Our study reveals that in the early stages of training, the number of usable pseudo labels is very low, which hampers the model's ability to converge quickly. Furthermore, throughout the training process, even though pseudo-labels are generated with high confidence, a significant number of labeling errors still occur. To address these challenges, we propose a novel framework, Generation-to-Optimization of Pseudo Labels (GO-PL), which comprehensively improves the model's performance in both the pseudo labels generation stage and the pseudo labels optimization stage. Specifically, we design a CLIP-based Pseudo Label Generation (CPLG) module, which matches proposals from the teacher model with CLIP text embeddings to generate semantically consistent high-confidence pseudo labels. These labels are further integrated into student training via an auxiliary loss, enabling early-stage guidance with stable and informative supervision signals. We also introduce Pixel-guided Instance Weighting (PIW) module that utilizes the SAM to assess the pixel-level quality of pseudo labels generated by the teacher model. Combined with a local voting mechanism, PIW dynamically assigns weights to each pseudo-label, thereby optimizing the impact of labels with varying quality on gradient updates. GO-PL demonstrates strong performance on standard SSOD benchmarks, including the MS-COCO and PASCAL VOC datasets. Notably, it achieves 26.7% mAP with only 1% labeled data on the MS-COCO, surpassing the previous best approach by 1.7%.
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