The current noise rate estimation approach may have limitations in handling more complex real-world scenarios due to several factors. One potential limitation is the assumption of a fixed noise rate throughout the training process, which may not accurately reflect the dynamic nature of noise in real-world datasets. To address this limitation, the model could be enhanced to adaptively update the noise rate estimation during training, taking into account the evolving nature of noise in the dataset.
Another limitation could be the identifiability issue in inferring the clean labels from noisy data, which may lead to multiple plausible solutions for the noise rate estimation. To mitigate this limitation, advanced techniques such as incorporating additional constraints or leveraging ensemble methods could be explored to improve the robustness and stability of the noise rate estimation.
Furthermore, the current approach may not fully capture the complex relationships between features, labels, and noise in the dataset, potentially leading to suboptimal noise rate estimates. Enhancements in the model architecture, such as incorporating more sophisticated feature representations or leveraging deep learning techniques for noise rate estimation, could help improve the accuracy and reliability of the estimation in challenging real-world scenarios.