On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks (1905.11001v5)

Abstract: Mixup~\cite{zhang2017mixup} is a recently proposed method for training deep neural networks where additional samples are generated during training by convexly combining random pairs of images and their associated labels. While simple to implement, it has been shown to be a surprisingly effective method of data augmentation for image classification: DNNs trained with mixup show noticeable gains in classification performance on a number of image classification benchmarks. In this work, we discuss a hitherto untouched aspect of mixup training -- the calibration and predictive uncertainty of models trained with mixup. We find that DNNs trained with mixup are significantly better calibrated -- i.e., the predicted softmax scores are much better indicators of the actual likelihood of a correct prediction -- than DNNs trained in the regular fashion. We conduct experiments on a number of image classification architectures and datasets -- including large-scale datasets like ImageNet -- and find this to be the case. Additionally, we find that merely mixing features does not result in the same calibration benefit and that the label smoothing in mixup training plays a significant role in improving calibration. Finally, we also observe that mixup-trained DNNs are less prone to over-confident predictions on out-of-distribution and random-noise data. We conclude that the typical overconfidence seen in neural networks, even on in-distribution data is likely a consequence of training with hard labels, suggesting that mixup be employed for classification tasks where predictive uncertainty is a significant concern.

• The paper demonstrates that mixup training significantly improves model calibration by generating soft labels that better represent uncertainty.
• The paper shows that mixup reduces overconfidence in DNN predictions, yielding more reliable outcomes on noisy and out-of-distribution data.
• The paper reveals mixup's broad applicability across image and NLP tasks, enhancing predictive reliability in diverse domains.

On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks

The paper entitled "On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks" provides an in-depth examination of the mixup training methodology, emphasizing its impact on model calibration and uncertainty in predictions. This paper presents a comprehensive exploration of mixup—a data augmentation technique where new samples are generated through convex combinations of image pairs and their corresponding labels—broadening its evaluation to include calibration and predictive confidence aspects beyond conventional performance metrics.

Key Contributions and Findings

The researchers identify and address a significant concern in deep neural networks (DNNs): overconfidence in predictions. This can lead to poor calibration, where the predicted probabilities are not reflective of true likelihoods, posing risks in high-stakes domains. The paper demonstrates that DNNs trained with mixup exhibit substantially better-calibrated scores, thereby reliably indicating the likelihood of prediction accuracy compared to standard training methods.

Notable highlights of the findings include:

• Improved Calibration: Across various architectures and datasets, including large-scale datasets like ImageNet, mixup enhances the calibration of neural networks. The paper establishes that merely mixing input features without adjusting labels does not account for the observed calibration benefits, underscoring the crucial role of label smoothing in mixup.
• Reduction in Overconfidence: Mixup training reduces the propensity for neural networks to make overly confident predictions, particularly on out-of-distribution and noisy data. This characteristic is beneficial for applications demanding high reliability in uncertain conditions.
• Consistency Across Different Data Types: The paper extends its analysis to natural language processing tasks, demonstrating that mixup can be applied beyond image classification, further solidifying its utility as a generalizable training strategy for improved uncertainty estimation.

Implications for Theory and Practice

The implications of these findings are significant. From a theoretical perspective, the results suggest the potential of mixup in steering neural network training towards learning truer posterior distributions by effectively integrating soft labels, which represent uncertainty more realistically during training. This contributes to the broader discourse on how training signal properties influence model behavior in predictions.

Practically, the ability of mixup to enhance predictive reliability advances its viability for integration into operational systems where incorrect predictions carry substantial risks. The incorporation of mixup could enhance decision-making systems in autonomous vehicles, healthcare diagnostics, and financial modeling by mitigating misplaced confidence in neural predictions.

Future Directions

The research opens up pathways for further exploration into how mixup can be leveraged alongside other calibration methods such as temperature scaling, dropout variabilities, and ensemble models to achieve optimally calibrated neural networks. Additionally, understanding how mixup interacts with adversarial training strategies remains an open avenue for investigation, potentially leading to models robust not only in calibration but also in adversarial settings.

Conclusively, "On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks" is a pivotal contribution that provides empirical evidence favoring the use of mixup, promoting robust and reliable predictive models in machine learning systems.