Spurious Feature Diversification Improves Out-of-distribution Generalization

TL;DR

This paper investigates how ensemble methods, especially WiSE-FT, improve out-of-distribution generalization by leveraging diverse spurious features, supported by theoretical analysis and empirical validation, and introduces a new averaging technique called BANG.

Contribution

It provides a new theoretical understanding of ensemble effectiveness in OOD settings and proposes a novel averaging method to further enhance performance.

Findings

WiSE-FT corrects many incorrect individual predictions, boosting OOD performance.

Ensemble models utilize diverse spurious features to reduce errors in OOD data.

The BANG averaging method significantly improves WiSE-FT's OOD generalization.

Abstract

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.