Accurate and Reliable Predictions with Mutual-Transport Ensemble

Authors: Han Liu, Peng Cui, Bingning Wang, Jun Zhu, Xiaolin Hu

Year: 2024

Source: https://arxiv.org/abs/2405.19656

TLDR:

The paper discusses the challenges of overconfidence in deep neural networks (DNNs) and the importance of reliable uncertainty estimation for model deployment in safety-critical applications. It introduces a new training method called Mutual-Transport Ensemble (MTE), which incorporates an auxiliary model and adaptive regularization using Kullback-Leibler (KL) divergence. MTE aims to improve both prediction accuracy and uncertainty calibration without the need for maintaining multiple models during inference, unlike Deep Ensembles (DE). The method is validated through extensive experiments on various benchmarks, demonstrating significant improvements in accuracy and uncertainty calibration. Additionally, the document discusses related work on single-model calibration methods, post-hoc calibration methods, and deep ensembles. It also explains the theoretical principles behind MTE and its potential extension to multiple auxiliary models. Overall, MTE offers a robust approach to enhancing the prediction accuracy and reliability of DNNs.

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The document introduces the Mutual-Transport Ensemble (MTE) method, which combines elements of regularization and ensemble techniques to improve the calibration and performance of deep neural networks, addressing the challenges of overconfidence and uncertainty estimation in safety-critical applications.

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Abstract

Deep Neural Networks (DNNs) have achieved remarkable success in a variety of tasks, especially when it comes to prediction accuracy. However, in complex real-world scenarios, particularly in safety-critical applications, high accuracy alone is not enough. Reliable uncertainty estimates are crucial. Modern DNNs, often trained with cross-entropy loss, tend to be overconfident, especially with ambiguous samples. To improve uncertainty calibration, many techniques have been developed, but they often compromise prediction accuracy. To tackle this challenge, we propose the ``mutual-transport ensemble'' (MTE). This approach introduces a co-trained auxiliary model and adaptively regularizes the cross-entropy loss using Kullback-Leibler (KL) divergence between the prediction distributions of the primary and auxiliary models. We conducted extensive studies on various benchmarks to validate the effectiveness of our method. The results show that MTE can simultaneously enhance both accuracy and uncertainty calibration. For example, on the CIFAR-100 dataset, our MTE method on ResNet34/50 achieved significant improvements compared to previous state-of-the-art method, with absolute accuracy increases of 2.4%/3.7%, relative reductions in ECE of $42.3%/29.4%, and relative reductions in classwise-ECE of 11.6%/15.3%.

Method

The paper introduces the "Mutual-Transport Ensemble" (MTE) method, which combines elements of regularization and ensemble techniques to improve the calibration and performance of deep neural networks, addressing the challenges of overconfidence and uncertainty estimation in safety-critical applications. MTE introduces a co-trained auxiliary model that utilizes the prediction distribution of the primary model as supervised labels and leverages Kullback-Leibler (KL) divergence as an adaptive regularizer. This method has been extensively tested on various benchmarks, demonstrating significant improvements in prediction accuracy and uncertainty calibration without incurring heavy computational overhead during inference.

Main Finding

The main finding of the paper is the introduction of the "Mutual-Transport Ensemble" (MTE) method, which effectively addresses the challenges of overconfidence and uncertainty estimation in deep neural networks (DNNs) by combining elements of regularization and ensemble techniques. MTE significantly improves both prediction accuracy and uncertainty calibration without incurring heavy computational overhead during inference, making it a promising approach for reliable model deployment in safety-critical applications.

Conclusion

The conclusion of this paper is that the "Mutual-Transport Ensemble" (MTE) method effectively addresses the challenges of overconfidence and uncertainty estimation in deep neural networks, improving both prediction accuracy and uncertainty calibration without incurring heavy computational overhead during inference. This conclusion is supported by extensive experiments on various benchmarks, demonstrating significant improvements in model performance and calibration.

Keywords

Mutual-Transport Ensemble, deep neural networks, uncertainty estimation, calibration, Kullback-Leibler divergence, auxiliary model, ensemble techniques, regularization, prediction accuracy, safety-critical applications, and benchmarking.

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