  As supervised learning still dominates most AI applications, test-time
performance is often unexpected. Specifically, a shift of the input covariates,
caused by typical nuisances like background-noise, illumination variations or
transcription errors, can lead to a significant decrease in prediction
accuracy. Recently, it was shown that incorporating self-supervision can
significantly improve covariate shift robustness. In this work, we propose
Self-Supervised Dynamic Networks (SSDN): an input-dependent mechanism, inspired
by dynamic networks, that allows a self-supervised network to predict the
weights of the main network, and thus directly handle covariate shifts at
test-time. We present the conceptual and empirical advantages of the proposed
method on the problem of image classification under different covariate shifts,
and show that it significantly outperforms comparable methods.
