  Generalization to out-of-distribution (OOD) data is a critical challenge in
machine learning. Ensemble-based methods, like weight space ensembles that
interpolate model parameters, have been shown to achieve superior OOD
performance. However, the underlying mechanism for their effectiveness remains
unclear. In this study, we closely examine WiSE-FT, a popular weight space
ensemble method that interpolates between a pre-trained and a fine-tuned model.
We observe an unexpected ``FalseFalseTrue" phenomenon, in which WiSE-FT
successfully corrects many cases where each individual model makes incorrect
predictions, which contributes significantly to its OOD effectiveness. To gain
further insights, we conduct theoretical analysis in a multi-class setting with
a large number of spurious features. Our analysis predicts the above phenomenon
and it further shows that ensemble-based models reduce prediction errors in the
OOD settings by utilizing a more diverse set of spurious features. Contrary to
the conventional wisdom that focuses on learning invariant features for better
OOD performance, our findings suggest that incorporating a large number of
diverse spurious features weakens their individual contributions, leading to
improved overall OOD generalization performance. Additionally, our findings
provide the first explanation for the mysterious phenomenon of weight space
ensembles outperforming output space ensembles in OOD. Empirically we
demonstrate the effectiveness of utilizing diverse spurious features on a
MultiColorMNIST dataset, and our experimental results are consistent with the
theoretical analysis. Building upon the new theoretical insights into the
efficacy of ensemble methods, we further propose a novel averaging method
called BAlaNced averaGing (BANG) which significantly enhances the OOD
performance of WiSE-FT.
