INNT: Restricting Activation Distance to Enhance Consistency of Visual Interpretation in Neighborhood Noise Training

Abstract

In this paper, we propose an end-to-end interpretable neighborhood noise training framework (INNT) to address the issue of inconsistent interpretations between clean and noisy samples in noise training. Noise training conventionally involves incorporating noisy samples into the training set, followed by generalization training. However, visual interpretations suggest that models may be learning the noise distribution rather than the desired robust target features. To mitigate this problem, we reformulate the noise training objective to minimize the visual interpretation consistency of images in the sample neighborhood. We design a noise activation distance constraint regularization term to enforce the similarity of high-level feature maps between clean and noisy samples. Additionally, we enhance the structure of noise training by iteratively resampling noise to more accurately depict the sample neighborhood. Furthermore, neighborhood noise is introduced to achieve more intuitive sample neighborhood sampling. Finally, we conducted qualitative and quantitative tests on different CNN architectures and public datasets. The results indicate that INNT leads to a more consistent decision rationale and balances the accuracy between noisy and clean samples.