Deep ensembles are capable of achieving state-of-the-art results in classification and out-of-distribution (OOD) detection. However, their effectiveness is limited due to the homogeneity of learned patterns within ensembles. To overcome this issue, our study introduces Saliency Diversified Deep Ensemble (SDDE), a novel approach that promotes diversity among ensemble members by leveraging saliency maps. Through incorporating saliency map diversification, our method outperforms conventional ensemble techniques and improves calibration in multiple classification and OOD detection tasks. In particular, the proposed method achieves state-of-the-art OOD detection quality, calibration, and accuracy on multiple benchmarks, including CIFAR10/100 and large-scale ImageNet datasets.

翻译：深度集成能够在分类和分布外检测中取得最先进的结果。然而，由于集成内部学习模式的同质性问题，其有效性受到限制。为解决这一问题，本研究引入了一种新颖方法——显著性多样化深度集成，通过利用显著性图促进集成成员间的多样性。通过融入显著性图多样化策略，我们的方法在多个分类和分布外检测任务中超越了传统集成技术，并改善了校准性能。特别地，所提方法在多个基准测试（包括CIFAR10/100和大规模ImageNet数据集）上实现了分布外检测质量、校准精度和准确率的最先进水平。