Distribution Mismatch Correction for Improved Robustness in Deep Neural Networks

TL;DR

This paper introduces an unsupervised distribution correction technique for deep neural networks that minimizes distribution mismatch during test time, significantly enhancing robustness against noise and corruptions without retraining.

Contribution

The authors propose a novel non-parametric, unsupervised distribution correction method that adapts layer activations to reduce training-test distribution mismatch using Wasserstein distance minimization.

Findings

Reduces impact of image corruptions on classification accuracy

Improves robustness without retraining or fine-tuning

Effective against intense noise and distribution shifts

Abstract

Deep neural networks rely heavily on normalization methods to improve their performance and learning behavior. Although normalization methods spurred the development of increasingly deep and efficient architectures, they also increase the vulnerability with respect to noise and input corruptions. In most applications, however, noise is ubiquitous and diverse; this can often lead to complete failure of machine learning systems as they fail to cope with mismatches between the input distribution during training- and test-time. The most common normalization method, batch normalization, reduces the distribution shift during training but is agnostic to changes in the input distribution during test time. This makes batch normalization prone to performance degradation whenever noise is present during test-time. Sample-based normalization methods can correct linear transformations of the activation distribution but cannot mitigate changes in the distribution shape; this makes the network vulnerable to distribution changes that cannot be reflected in the normalization parameters. We propose an unsupervised non-parametric distribution correction method that adapts the activation distribution of each layer. This reduces the mismatch between the training and test-time distribution by minimizing the 1-D Wasserstein distance. In our experiments, we empirically show that the proposed method effectively reduces the impact of intense image corruptions and thus improves the classification performance without the need for retraining or fine-tuning the model.