Self-Supervised Dynamic Networks for Covariate Shift Robustness

Understand

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.