🤖 AI Summary
To address performance degradation caused by label noise in ordinal image classification, this paper proposes ORDAC—the first framework to integrate Label Distribution Learning (LDL) into ordinal noise-robust modeling. ORDAC employs a data-centering adaptive mechanism to dynamically estimate and optimize the mean and standard deviation of each sample’s label distribution, enabling differentiable, fine-grained label correction—rather than discarding suspicious samples—under both symmetric and asymmetric Gaussian noise assumptions. Experiments on the Adience and Diabetic Retinopathy datasets demonstrate significant improvements: on Adience with 40% label noise, the mean absolute error decreases from 0.86 to 0.62, and recall rises from 0.37 to 0.49; moreover, ORDAC effectively mitigates inherent noise present in the original data. By unifying ordinal constraints with distributional label modeling, ORDAC establishes a novel paradigm for noise-robust learning in ordinal classification.
📝 Abstract
Labeled data is a fundamental component in training supervised deep learning models for computer vision tasks. However, the labeling process, especially for ordinal image classification where class boundaries are often ambiguous, is prone to error and noise. Such label noise can significantly degrade the performance and reliability of machine learning models. This paper addresses the problem of detecting and correcting label noise in ordinal image classification tasks. To this end, a novel data-centric method called ORDinal Adaptive Correction (ORDAC) is proposed for adaptive correction of noisy labels. The proposed approach leverages the capabilities of Label Distribution Learning (LDL) to model the inherent ambiguity and uncertainty present in ordinal labels. During training, ORDAC dynamically adjusts the mean and standard deviation of the label distribution for each sample. Rather than discarding potentially noisy samples, this approach aims to correct them and make optimal use of the entire training dataset. The effectiveness of the proposed method is evaluated on benchmark datasets for age estimation (Adience) and disease severity detection (Diabetic Retinopathy) under various asymmetric Gaussian noise scenarios. Results show that ORDAC and its extended versions (ORDAC_C and ORDAC_R) lead to significant improvements in model performance. For instance, on the Adience dataset with 40% noise, ORDAC_R reduced the mean absolute error from 0.86 to 0.62 and increased the recall metric from 0.37 to 0.49. The method also demonstrated its effectiveness in correcting intrinsic noise present in the original datasets. This research indicates that adaptive label correction using label distributions is an effective strategy to enhance the robustness and accuracy of ordinal classification models in the presence of noisy data.