关键词: 重尾噪声, 图像分类, 残差网络, 多分布, 深度学习

Abstract: To address the problem where image classification accuracy of ResNet （Residual Network） drops due to unknown heavy-tailed noise, a Multi-distribution Heavy-Tailed Noise Adaptive ResNet (MHTNA-ResNet) model was proposed. Firstly, to mitigate the impact of heavy-tailed noise on final predictions, a Multi-distribution Heavy-Tailed Noise Adaptive Layer (MHTNA) was designed, which created noise templates using various heavy-tailed distributions to perturb clean training data, enabling ResNet to learn recognition capabilities for heavy-tailed noisy images through training. Secondly, MHTNA was adaptively trained up-dating noise templates and using the maximum likelihood estimation method to solve for noise template parameters. Then, noise templates were regenerated based on these parameters to ensure adherence to a heavy-tailed distribution. Finally, during testing, the MHTNA was masked to simulate heavy-tailed noise attacks on test images, thereby evaluating the model's capability of noise resistance. Compared to the PRIME method, the classification accuracy of MHTNA-ResNet improves by an average of 3.94, 7.09 and 5.46 percentage points on the CIFAR-10, CIFAR-100 and Mini-ImageNet datasets when faced with heavy-tailed noise attacks. The experimental results demonstrate that the proposed method can effectively improve ResNet's robustness against heavy-tailed noise interference. Abstract: To address the problem where image classification accuracy of ResNet （Residual Network） drops due to unknown heavy-tailed noise, a Multi-distribution Heavy-Tailed Noise Adaptive ResNet (MHTNA-ResNet) model was proposed. Firstly, to mitigate the impact of heavy-tailed noise on final predictions, a Multi-distribution Heavy-Tailed Noise Adaptive Layer (MHTNA) was designed, which created noise templates using various heavy-tailed distributions to perturb clean training data, enabling ResNet to learn recognition capabilities for heavy-tailed noisy images through training. Secondly, MHTNA was adaptively trained up-dating noise templates and using the maximum likelihood estimation method to solve for noise template parameters. Then, noise templates were regenerated based on these parameters to ensure adherence to a heavy-tailed distribution. Finally, during testing, the MHTNA was masked to simulate heavy-tailed noise attacks on test images, thereby evaluating the model's capability of noise resistance. Compared to the PRIME method, the classification accuracy of MHTNA-ResNet improves by an average of 3.94, 7.09 and 5.46 percentage points on the CIFAR-10, CIFAR-100 and Mini-ImageNet datasets when faced with heavy-tailed noise attacks. The experimental results demonstrate that the proposed method can effectively improve ResNet's robustness against heavy-tailed noise interference. 

Key words: heavy-tailed noise, image classification, Residual Network (ResNet), multi-distribution, deep learning