@inproceedings{
lion2023how,
title={How Good is a Single Basin?},
author={Kai Lion and Gregor Bachmann and Lorenzo Noci and Thomas Hofmann},
booktitle={UniReps:  the First Workshop on Unifying Representations in Neural Models},
year={2023},
url={https://openreview.net/forum?id=JYww68b9PA}
}
The multi-modal nature of neural loss landscapes is often considered to be the main driver behind the empirical success of deep ensembles. In this work, we probe this belief by constructing various "connected" ensembles which are restricted to lie in the same basin. Through our experiments, we demonstrate that increased connectivity indeed negatively impacts performance. However, when incorporating the knowledge from other basins implicitly through distillation, we show that the gap in performance can be mitigated by re-discovering (multi-basin) deep ensembles in a single basin. Thus, we conjecture that while the extra-basin knowledge is at least partially present in any given basin, it cannot be easily harnessed without learning it from other basins.
How Good is a Single Basin?
Kai Lion Gregor Bachmann Lorenzo Noci Thomas Hofmann