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label correction

Label correction is a machine learning process that identifies and updates inaccurate, noisy, or mismatched ground-truth annotations within a training dataset. Rather than discarding mislabeled samples or solely relying on loss functions designed to tolerate noise, label correction methods actively re-estimate and replace erroneous class labels or correspondence targets with more reliable values. These adjustments are commonly performed during dataset preprocessing or iteratively throughout model training using mechanisms such as model prediction confidence, neighborhood consistency, or meta-learning frameworks. By rectifying incorrect annotations instead of filtering them out, label correction preserves valuable training data, prevents models from memorizing spurious patterns, and improves overall generalization and accuracy across various tasks.

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Noisy Correspondence Learning with Meta Similarity Correction

Noisy Correspondence Learning with Meta Similarity Correction

Haochen Han, Kaiyao Miao, Qinghua Zheng, Minnan Luo

OrganizationsXi'an Jiaotong University

Why you should read this

Proposes a meta-learning framework that trains a correction network on clean and mismatched meta-data to rectify similarity scores and filter out mismatched cross-modal pairs during retrieval training.

Despite the success of multimodal learning in cross-modal retrieval task, the remarkable progress relies on the correct correspondence among multimedia data. However, collecting such ideal data is expensive and time-consuming. In practice, most widely used datasets are harvested from the Internet and inevitably contain mismatched pairs. Training on such noisy correspondence datasets causes performance degradation because the cross-modal retrieval methods can wrongly enforce the mismatched data to be similar. To tackle this problem, we propose a Meta Similarity Correction Network (MSCN) to provide reliable similarity scores. We view a binary classification task as the meta-process that encourages the MSCN to learn discrimination from positive and negative meta-data. To further alleviate the influence of noise, we design an effective data purification strategy using meta-data as prior knowledge to remove the noisy samples. Extensive experiments are conducted to demonstrate the strengths of our method in both synthetic and real-world noises, including Flickr30K, MS-COCO, and Conceptual Captions. Our code is publicly available.

Added

2026-09-26