Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels

• URL: http://arxiv.org/abs/2505.13342v1
• Date: Mon, 19 May 2025 16:49:27 GMT
• Title: Detect and Correct: A Selective Noise Correction Method for Learning with Noisy Labels
• Authors: Yuval Grinberg, Nimrod Harel, Jacob Goldberger, Ofir Lindenbaum,
• Abstract summary: Falsely annotated samples, also known as noisy labels, can significantly harm the performance of deep learning models.<n>Two main approaches for learning with noisy labels are global noise estimation and data filtering.<n>Our method identifies potentially noisy samples based on their loss distribution.<n>We then apply a selection process to separate noisy and clean samples and learn a noise transition matrix to correct the loss for noisy samples while leaving the clean data unaffected.
• Score: 14.577138753507203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Falsely annotated samples, also known as noisy labels, can significantly harm the performance of deep learning models. Two main approaches for learning with noisy labels are global noise estimation and data filtering. Global noise estimation approximates the noise across the entire dataset using a noise transition matrix, but it can unnecessarily adjust correct labels, leaving room for local improvements. Data filtering, on the other hand, discards potentially noisy samples but risks losing valuable data. Our method identifies potentially noisy samples based on their loss distribution. We then apply a selection process to separate noisy and clean samples and learn a noise transition matrix to correct the loss for noisy samples while leaving the clean data unaffected, thereby improving the training process. Our approach ensures robust learning and enhanced model performance by preserving valuable information from noisy samples and refining the correction process. We applied our method to standard image datasets (MNIST, CIFAR-10, and CIFAR-100) and a biological scRNA-seq cell-type annotation dataset. We observed a significant improvement in model accuracy and robustness compared to traditional methods.

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