Deep ensembles (DE) have been successful in improving model performance by
learning diverse members via the stochasticity of random initialization. While
recent works have attempted to promote further diversity in DE via
hyperparameters or regularizing loss functions, these methods primarily still
rely on a stochastic approach to explore the hypothesis space. In this work, we
present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse
ensembles by capturing the multiplicity of hypotheses along symmetry axes,
which explore the hypothesis space beyond stochastic perturbations of model
weights and hyperparameters. We leverage recent advances in contrastive
representation learning to create models that separately capture opposing
hypotheses of invariant and equivariant symmetries and present a simple
ensembling approach to efficiently combine appropriate hypotheses for a given
task. We show that MSE effectively captures the multiplicity of conflicting
hypotheses that is often required in large, diverse datasets like ImageNet. As
a result of their inherent diversity, MSE improves classification performance,
uncertainty quantification, and generalization across a series of transfer
tasks.

本研究提出了一种名为 Multi-Symmetry Ensembles (MSE) 的框架，通过捕获对称轴上假设的多样性来构建多样化的集合，探索超越随机变化的模型权重和超参数假设空间并有效提高了分类性能、不确定性量化和迁移任务的泛化。