Spurious correlations in the data, where multiple cues are predictive of the
target labels, often lead to a phenomenon known as simplicity bias, where a
model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In
this work, we propose an ensemble diversification framework exploiting
Diffusion Probabilistic Models (DPMs) for shortcut bias mitigation. We show
that at particular training intervals, DPMs can generate images with novel
feature combinations, even when trained on images displaying correlated input
features. We leverage this crucial property to generate synthetic
counterfactuals to increase model diversity via ensemble disagreement. We show
that DPM-guided diversification is sufficient to remove dependence on primary
shortcut cues, without a need for additional supervised signals. We further
empirically quantify its efficacy on several diversification objectives, and
finally show improved generalization and diversification performance on par
with prior work that relies on auxiliary data collection.

在这项工作中，我们提出了一种利用扩散概率模型（DPMs）来减轻简洁性偏见的集成多样化框架。我们展示了在特定训练间隔中，DPMs 能够生成具有新颖特征组合的图像，即使在训练时显示相关输入特征的图像。我们利用这一关键属性通过集成差异来生成合成的反事实情景，从而增加模型的多样性。我们展示了 DPM 引导的多样化足以去除对主要简洁性提示的依赖，而无需额外的监督信号。我们进一步通过几个多样化目标进行了实证量化，并最终展示了与依赖辅助数据收集的先前工作相当的改进的泛化和多样化性能。

使用扩散概率模型的集合多样性减少快速选择偏差

Shortcut Bias Mitigation via Ensemble Diversity Using Diffusion  Probabilistic Models

Spurious correlations in the data, where multiple cues are predictive of the
target labels, often lead to shortcut learning phenomena, where a model may
rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this
work, we propose an ensemble diversification framework exploiting the
generation of synthetic counterfactuals using Diffusion Probabilistic Models
(DPMs). We discover that DPMs have the inherent capability to represent
multiple visual cues independently, even when they are largely correlated in
the training data. We leverage this characteristic to encourage model diversity
and empirically show the efficacy of the approach with respect to several
diversification objectives. We show that diffusion-guided diversification can
lead models to avert attention from shortcut cues, achieving ensemble diversity
performance comparable to previous methods requiring additional data
collection.

通过使用扩散概率模型生成合成的反事实情况，我们提出了一种集成多样化框架，用于处理数据中的错误相关性和捷径学习现象，实验证明扩散引导的多样化能够使模型避开捷径线索，获得与额外数据采集方法相当的集成多样性性能。