Existing Out-of-Distribution (OoD) detection methods address to detect OoD
samples from In-Distribution data (InD) mainly by exploring differences in
features, logits and gradients in Deep Neural Networks (DNNs). We in this work
propose a new perspective upon loss landscape and mode ensemble to investigate
OoD detection. In the optimization of DNNs, there exist many local optima in
the parameter space, or namely modes. Interestingly, we observe that these
independent modes, which all reach low-loss regions with InD data (training and
test data), yet yield significantly different loss landscapes with OoD data.
Such an observation provides a novel view to investigate the OoD detection from
the loss landscape and further suggests significantly fluctuating OoD detection
performance across these modes. For instance, FPR values of the RankFeat method
can range from 46.58% to 84.70% among 5 modes, showing uncertain detection
performance evaluations across independent modes. Motivated by such diversities
on OoD loss landscape across modes, we revisit the deep ensemble method for OoD
detection through mode ensemble, leading to improved performance and benefiting
the OoD detector with reduced variances. Extensive experiments covering varied
OoD detectors and network structures illustrate high variances across modes and
also validate the superiority of mode ensemble in boosting OoD detection. We
hope this work could attract attention in the view of independent modes in the
OoD loss landscape and more reliable evaluations on OoD detectors.

通过对损失景观和模态集进行研究，提出了一种新的视角来调查面向损失景观的异常样本检测，重访了深度集成方法以提高异常检测性能和减小变异，并通过广泛的实验证明了模态的高度变化和模态集成在提升异常检测中的优越性。