Data quality is essential for the performance of deep neural networks in various fields. However, label noise and class imbalance are common data issues, which cause deep learning models to overfit in real-world scenarios. Recent research solves the learning with noisy labels (LNL) problem by employing label correction or loss adjustment methods, which often rely on uncertainty estimation. Unfortunately, these methods usually do not work well with imbalanced datasets. To address both noisy and imbalanced data biases, we analyze the limitations of current discriminative models in uncertainty estimation, and propose Class-aware Multi-granularity Co-Diffusion models (CaMCoD), which leverage a generative uncertainty and inconsistency loss adjustment method to generate labels more robustly. Specifically, we reframe the LNL problem as a robust diffusion-generative process, i.e., labels are generated by gradually refining an initial random guess. First, we use coarse-grained uncertainty from the diffusion model to achieve more accurate confidence estimates. This will guide the model to generate correct labels on a broader level. Then, we leverage the fine-grained inconsistency of co-diffusion models during reverse denoising to determine the learnable weight for each sample, which can mitigate the risk of the model overfitting to noisy samples. Finally, we apply class-aware loss adjustments to reduce data bias caused by class imbalance. Experiments on both synthetic and real-world datasets demonstrate that our method perform well in imbalanced and noisy scenarios. We provide our code on GitHub:https://github.com/SenyuHou/CaMCoD.

Class overlap is a major factor of data complexity that hampers classifier performance, particularly in imbalanced learning scenarios. Most existing oversampling methods rely on conservative seed sample selection and decoupled synthesis strategies, which limit sample diversity and fail to effectively control overlap risk. This paper proposes a novel oversampling framework called TMACO (Class Alliance-Constrained Oversampling), which integrates data complexity considerations into both seed selection and sample generation. First, TMACO selects seed sample units using a class alliance constraint that jointly considers spatial geometry and class distribution to enhance diversity and representativeness. Second, it generates synthetic samples based on three-point units to ensure regional stability. Third, a region-level filtering mechanism is applied to prevent synthetic samples from intruding into majority class areas. Extensive experiments on benchmark and real-world datasets demonstrate that TMACO consistently improves minority class performance and overall classification accuracy compared to state-of-the-art oversampling techniques. The proposed method also offers interpretable parameter control and adapts well to varying task objectives.