Model-agnostic information transfer and fusion for classification with label noise
The authors developed a generic framework to improve classification when large datasets have noisy labels by using a small, clean expert dataset. The method purifies the large noisy data through Bayes Optimal extraction and handles uncertain boundary samples using either active learning or pseudo-tagging. This provides a robust, model-agnostic way to fuse information from both data sources.