M. Hilbert

From Faults to Features: Pretraining to Learn Robust Representations against Sensor Failures

This work proposes a self-supervised masking scheme that simulates common sensor failures and explicitly trains the model to recover the original signal, and demonstrates that the resulting representations significantly improve the robustness of predictions to seen and unseen sensor failures on a vehicle dynamics dataset.