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Extraneous variables are variables that are irrelevant for a certain task,but heavily affect the distribution of the available data. In this work, weshow that the presence of such variables can degrade the performance ofdeep-learning models. We study three datasets where there is a strong influenceof known extraneous variables: classification of upper-body movements in strokepatients, annotation of surgical activities, and recognition of corruptedimages. Models trained with batch normalization learn features that are highlydependent on the extraneous variables. In batch normalization, the statisticsused to normalize the features are learned from the training set and fixed attest time, which produces a mismatch in the presence of varying extraneousvariables. We demonstrate that estimating the feature statistics adaptivelyduring inference, as in instance normalization, addresses this issue, producingnormalized features that are more robust to changes in the extraneousvariables. This results in a significant gain in performance for differentnetwork architectures and choices of feature statistics.

Be Like Water: Robustness to Extraneous Variables Via Adaptive Feature Normalization