Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation (1906.02611v1)

Abstract: Deploying machine learning systems in the real world requires both high accuracy on clean data and robustness to naturally occurring corruptions. While architectural advances have led to improved accuracy, building robust models remains challenging. Prior work has argued that there is an inherent trade-off between robustness and accuracy, which is exemplified by standard data augment techniques such as Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image. Models trained with Patch Gaussian achieve state of the art on the CIFAR-10 and ImageNetCommon Corruptions benchmarks while also improving accuracy on clean data. We find that this augmentation leads to reduced sensitivity to high frequency noise(similar to Gaussian) while retaining the ability to take advantage of relevant high frequency information in the image (similar to Cutout). Finally, we show that Patch Gaussian can be used in conjunction with other regularization methods and data augmentation policies such as AutoAugment, and improves performance on the COCO object detection benchmark.

• The paper introduces Patch Gaussian, a novel augmentation method that combines localized Gaussian noise with Cutout to optimize both robustness and clean accuracy.
• Experimental results on benchmarks like CIFAR-10, ImageNet, and COCO show that models trained with Patch Gaussian achieve state-of-the-art performance on both clean and corrupted data.
• The method challenges the traditional trade-off between robustness and accuracy, opening new avenues for integrating augmentation with other regularization techniques in robust AI.

Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation

The paper "Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation" introduces a novel data augmentation strategy aimed at enhancing the robustness of machine learning models against naturally occurring corruptions while maintaining or even improving their accuracy on clean data. This strategy, named Patch Gaussian, emerges as a response to the documented trade-off between robustness and accuracy observed with standard data augmentation techniques, such as Gaussian noise and Cutout.

Key Insights

The authors presented Patch Gaussian, a method which combines Gaussian noise augmentation with a spatially localized focus akin to Cutout, effectively balancing the strengths of both techniques. Unlike Gaussian noise, which improves robustness at the expense of clean accuracy, or Cutout, which boosts clean data performance without aiding robustness, Patch Gaussian efficiently navigates this trade-off.

Through rigorous experimentation with standard image classification benchmarks such as CIFAR-10 and ImageNet, the authors demonstrate that models trained with Patch Gaussian exhibit state-of-the-art performance on both clean accuracy and corrupted data benchmarks, namely CIFAR-C and ImageNet-C. The augmentation involves selectively applying Gaussian noise to random patches within the image data, which leads to reduced sensitivity to high-frequency perturbations while harnessing relevant high-frequency information for improved generalization.

Significance of Numerical Results

Patch Gaussian's efficacy is quantitatively supported by its performance on established benchmarks. The paper reports that on CIFAR-10 and ImageNet, models trained with this augmentation achieve not only superior mean Corruption Error (mCE) rates but also match or exceed the clean accuracy of traditional augmentation methods. For instance, the ResNet-50 model trained on ImageNet demonstrates an mCE improvement to 0.872 from 1.00 (baseline), underscores the augmentation's efficacy in handling real-world corruptions without compromising accuracy.

Furthermore, the research extends the application of Patch Gaussian to object detection tasks, where it notably enhances the performance on COCO validation sets, further attesting to its versatility across different machine learning tasks beyond image classification.

Implications and Future Work

From a theoretical perspective, Patch Gaussian suggests a nuanced approach to model training where noise robustness doesn’t necessitate a trade-off with accuracy. This insight challenges the conventional belief in the inherent dichotomy between robustness and accuracy, inviting further exploration into augmentation strategies that jointly optimize these attributes.

Practically, the method’s compatibility with other regularization techniques and augmentation policies like AutoAugment underscores its potential for wide applicability in various domains demanding resilient and precise models.

This paper paves the way for subsequent research to delve into more sophisticated augmentation techniques that intelligently balance noise robustness with contextual stimulus, perhaps exploring the principles of natural image statistics or integrating domain-specific priors to engineer invariant yet sensitive models.

In conclusion, the Patch Gaussian augmentation demonstrates an elegant solution to the robustness-accuracy trade-off in neural networks, propelling advancements in our understanding of model generalization and robustness under practical conditions. The insights from this work could inform future developments in robust AI systems that maintain high performance in diverse and unpredictable environments.