Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning
This work addresses noisy label learning, a common issue in machine learning, with an incremental improvement in method utilization.
The paper tackles the problem of learning with noisy labels by proposing a new resampling method, RENT, which utilizes the transition matrix more effectively than previous reweighting approaches, achieving consistent performance improvements across multiple benchmark datasets.
For learning with noisy labels, the transition matrix, which explicitly models the relation between noisy label distribution and clean label distribution, has been utilized to achieve the statistical consistency of either the classifier or the risk. Previous researches have focused more on how to estimate this transition matrix well, rather than how to utilize it. We propose good utilization of the transition matrix is crucial and suggest a new utilization method based on resampling, coined RENT. Specifically, we first demonstrate current utilizations can have potential limitations for implementation. As an extension to Reweighting, we suggest the Dirichlet distribution-based per-sample Weight Sampling (DWS) framework, and compare reweighting and resampling under DWS framework. With the analyses from DWS, we propose RENT, a REsampling method with Noise Transition matrix. Empirically, RENT consistently outperforms existing transition matrix utilization methods, which includes reweighting, on various benchmark datasets. Our code is available at \url{https://github.com/BaeHeeSun/RENT}.