GaussianMix: Rethinking Receptive Field for Efficient Data Augmentation

Abstract

Mixed Sample Data Augmentation (MSDA) enhances deep learning model generalization by blending a source patch into a target image. Selecting source patches based on image saliency helps to prevent label errors and irrelevant content; however, it relies on computationally expensive saliency detection algorithms. Studies suggest that a convolutional neural network’s receptive field follows a Gaussian distribution, with central pixels being more influential. Leveraging this, we propose GaussianMix, an effective and efficient augmentation strategy that selects source patches using a center-biased Gaussian distribution in order to avoiding additional computational costs. GaussianMix achieves top-1 error rates of 21.26% and 20.09% on ResNet-50 and ResNet-101 for ImageNet classification, respectively, while also improving robustness against adversarial perturbations and enhancing object detection performance.

Keywords

• data augmentation
• receptive field
• CNN