The concept of noise rate estimation can be integrated into other machine learning tasks beyond noisy-label learning, such as domain adaptation or few-shot learning, to enhance model performance and generalization in the presence of label noise.
In domain adaptation, estimating the noise rate between the source and target domains can help adapt the model more effectively by adjusting the learning process based on the level of noise present in the data distribution shift. By incorporating noise rate estimation into domain adaptation algorithms, the model can better align the source and target domains, leading to improved transfer learning performance.
In few-shot learning, estimating the noise rate in the few-shot training data can aid in selecting reliable support samples and reducing the impact of noisy labels on the model's ability to generalize to unseen classes. By leveraging noise rate estimation techniques, few-shot learning algorithms can better identify and utilize clean samples for effective model training with limited labeled data.
Overall, integrating noise rate estimation into various machine learning tasks can enhance model robustness, improve generalization performance, and mitigate the adverse effects of label noise in diverse learning scenarios.