Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data
Overview:
It is important that methods used in safety-critical settings know when they are likely to fail. Therefore, uncertainty estimation in deep learning is a key field for applications such as robotics. This method, named GammaSSL, trains a segmentation network that can estimate when it is likely to fail as a result of distributional shift - i.e. performs distributional uncertainty estimation. It achieves this by using uncurated, unlabelled out-of-distribution training images.
Link to $\mathrm{ar\chi iv}$ and $\mathrm{code}$.
BibTeX:
@article{williams2024gammassl,
author = {Williams, David and De Martini, Daniele and Gadd, Matthew and Newman, Paul},
title = {Mitigating Distributional Shift in Semantic Segmentation via Uncertainty Estimation from Unlabelled Data},
journal = {IEEE Transactions on Robotics (T-RO)},
year = {2024},
}