Deep visual recognition models have achieved remarkable success in diverse domains ranging from everyday object classification to specialized tasks，such as medical image analysis. However，their performance remains heavily dependent on large-scale labeled datasets，the construction of which is costly，time consuming，and often impractical in real-world scenarios. Semi-supervised learning （SSL），which leverages abundant unlabeled data to compensate for limited labeled samples，has therefore emerged as a promising paradigm. Among existing SSL methods，FixMatch is a widely adopted baseline because of its simple yet powerful combination of consistency regularization and pseudo-labeling，driven by weak-strong augmentation. However，FixMatch still faces notable limitations： feature extraction instability during early training，entanglement of content and style caused by acquisition or imaging variations，and noise accumulation from single-view pseudo-labels. These issues reduce the robustness and generalizability of FixMatch in realistic deployment scenarios.

To address these challenges，we propose FixMatch++，an enhanced framework that introduces three tightly integrated improvements. The first improvement is learnable-shift batch normalization （LS-BN） and dual-scale parallel convolution （DSPC）. Conventional batch normalization assumes stable channel statistics，but the aggressive augmentations employed in FixMatch frequently distort these distributions，introducing bias. We design LS-BN that adaptively corrects augmentation-induced deviations，and DSPC incorporates parallel convolutional branches with different receptive fields，thus enabling rich multiscale representations and improving robustness to variations in object size and texture. The second improvement is content-style dual-branch representation with dynamic residual gating. Natural images often embed intertwined structural （content） and appearance （style） factors. A single-stream backbone entangles these signals，reducing discriminative power. We disentangle features into two dedicated branches： one focusing on content （shape and semantics） and the other on style （texture，illumination，and acquisition artifacts）. A dynamic residual gating mechanism adaptively fuses the two streams and selectively emphasizes informative features under varying conditions，thereby stabilizing representation learning and enhancing generalizability. The third improvement is multilevel pseudo-label fusion. Unlike FixMatch that generates pseudo-labels from a single augmented view，FixMatch++ aggregates predictions from weak，medium，and strong augmentations. Their class probabilities are fused through weighted averaging，ensuring that noisy predictions from any single view are mitigated by corroborating evidence from others. In addition，we introduce a class-wise confidence thresholding strategy，which applies tailored thresholds for different categories to prevent dominant classes from overwhelming minority ones and to ensure balanced pseudo-label adoption. Together，these mechanisms substantially increase the quantity and quality of pseudo-labels，which are critical for effective semi-supervised training.

We conduct extensive experiments on three widely used SSL benchmarks： CIFAR-10，CIFAR-100，and SVHN. We benchmark FixMatch++ against seven state-of-the-art baselines，namely，MeanTeacher，UDA，ReMixMatch，FixMatch，FlexMatch，SoftMatch，and UES. Across all datasets，FixMatch++ consistently achieves the lowest classification error rates among the approaches. Gains are particularly pronounced in low-label regimes，which best reflect real-world annotation scarcity. For instance，under the challenging CIFAR-10 setting with only 250 labeled samples，FixMatch++ reduces the error rate to 4.56%，representing an absolute improvement of 0.35%—27.76% compared with the seven baselines. On CIFAR-100，which involves 100 categories and is highly susceptible to interclass confusion，FixMatch++ demonstrates clear adaptability，outperforming FlexMatch and ReMixMatch. On SVHN，which features digit recognition in complex street-view conditions with substantial imaging variability，our framework surpasses UDA and SoftMatch，validating its robustness to acquisition artifacts. We conduct ablation studies to further validate the contribution of each proposed component. Results show that removing learnable-shift batch normalization considerably increases the error rates，confirming the necessity of correcting augmentation-induced biases. Excluding the content-style disentanglement module leads to unstable training and increased sensitivity to domain shifts，revealing the importance of separating and adaptively fusing features. Eliminating multilevel pseudo-label fusion degrades label quality and amplifies noise，particularly at the late training stages，thereby verifying the effectiveness of the fusion strategy. We also perform visualization analyses，including feature map inspection，class activation mapping，and pseudo-label confidence distribution plots. These visualizations provide intuitive evidence of how FixMatch++ stabilizes feature extraction，disentangles factors of variation，and generates reliable pseudo-labels. Together，the analyses confirm not only the empirical improvements but also the interpretability of the model’s internal processes.

In summary，FixMatch++ substantially advances semi-supervised image classification by introducing improvements at the architectural and algorithmic levels. The learnable-shift batch normalization and dual-scale convolution modules enhance the robustness of feature extraction. The content-style dual-branch representation with dynamic residual gating disentangles and adaptively fuses complementary features，and the multilevel pseudo-label fusion mechanism ensures high-quality supervision from unlabeled data. Comprehensive experiments and analyses demonstrate that these improvements yield consistent and notable performance gains over strong SSL baselines across multiple datasets and labeling regimes. Owing to the modular design of FixMatch++，the proposed components can be seamlessly integrated into other SSL frameworks，extending the applicability and influence of FixMatch++. FixMatch++ enlarges the effective training set under limited-label conditions and strengthens the reliability and interpretability of SSL. It offers a practical solution for real-world scenarios where annotation resources are scarce yet robust performance is essential.