Rank consistent ordinal regression for neural networks with application to age estimation (1901.07884v7)

Abstract: In many real-world prediction tasks, class labels include information about the relative ordering between labels, which is not captured by commonly-used loss functions such as multi-category cross-entropy. Recently, the deep learning community adopted ordinal regression frameworks to take such ordering information into account. Neural networks were equipped with ordinal regression capabilities by transforming ordinal targets into binary classification subtasks. However, this method suffers from inconsistencies among the different binary classifiers. To resolve these inconsistencies, we propose the COnsistent RAnk Logits (CORAL) framework with strong theoretical guarantees for rank-monotonicity and consistent confidence scores. Moreover, the proposed method is architecture-agnostic and can extend arbitrary state-of-the-art deep neural network classifiers for ordinal regression tasks. The empirical evaluation of the proposed rank-consistent method on a range of face-image datasets for age prediction shows a substantial reduction of the prediction error compared to the reference ordinal regression network.

• The paper proposes the CORAL framework that converts ordinal regression into binary classification tasks with guaranteed rank monotonicity.
• It leverages CNN architectures like ResNet-34 to demonstrate significant improvements in MAE and RMSE on diverse age estimation datasets.
• The study highlights CORAL's potential for broad applications in ordinal prediction tasks beyond age estimation, ensuring efficient and consistent performance.

Analyzing the CORAL Framework for Ordinal Regression with Neural Networks

The paper "Rank consistent ordinal regression for neural networks with application to age estimation" presents a novel approach to ordinal regression, particularly enhancing neural networks' capabilities in predicting ordinal scales. The authors propose the COnsistent RAnk Logits (CORAL) framework, which aims to resolve the inconsistencies encountered in previous ordinal regression methods that rely on binary classification tasks. Previous methodologies, particularly the Ordinal Regression CNN (OR-CNN) by \cite{niu2016ordinal}, struggled with classifier inconsistencies that compromised predictive accuracy.

Methodology and Theoretical Contribution

Ordinal regression differentiates itself from traditional classification by acknowledging the inherent order within the class labels but without assuming equidistant spacing. Addressing the shortcomings in existing methods, the CORAL framework represents a crucial development, ensuring rank monotonicity and consistent confidence scores. It innovatively transforms ordinal regression targets into binary classification subtasks with robust theoretical guarantees of prediction consistency.

The theoretical foundation of CORAL is underscored by a critical theorem. This theorem provides guarantees for the ordered nature of the learned bias units in the model's output layer, ensuring non-increasing order among them. This arrangement results in consistently ranked predictions across the binary classification tasks. Through sharing weight parameters with independent bias units across these tasks, CORAL maintains efficiency without burdening the computational complexity, a problem prevalent in prior implementations.

Empirical Evaluation

The empirical evaluation of the CORAL framework was executed on multiple age-prediction datasets using convolutional neural networks (CNNs), particularly leveraging the ResNet-34 architecture. The experimentation included datasets such as MORPH-2, AFAD, and CACD, with face images ranging from various age brackets. Across all tested benchmarks, the CORAL-CNN demonstrated a notable improvement in predictive performance compared to the baseline methods, including the standard cross-entropy classifiers and the OR-CNN. Metrics such as mean absolute error (MAE) and root mean squared error (RMSE) substantiated these performance gains, showcasing CORAL’s efficacy in maintaining rank consistency and reducing error rates.

Implications and Future Directions

The implications of this research are twofold. Practically, the CORAL framework can be directly applied to any ordinal regression tasks that require understanding ordered relationships among target variables, extending beyond age estimation to fields such as customer satisfaction ratings, biological cell counting, and crowd density estimation. Theoretically, CORAL contributes significantly to the machine learning landscape by offering a robust solution to maintain consistency within an ordinal prediction context, inviting further exploration into other architectures beyond CNNs, such as recurrent neural networks (RNNs) or transformer models.

In conclusion, the CORAL framework proposed in this paper presents a significant advancement in the field of ordinal regression for neural networks. By guaranteeing classifier consistency and achieving substantial performance gains across various datasets, CORAL positions itself as an efficient, architecture-agnostic solution ready to address complex ordinal regression challenges in modern artificial intelligence applications. The paper's contributions provide a solid foundation for future developments in leveraging deep learning for ordered data prediction tasks.