研究人员开发了一个名为RefCal的新框架，以提高深度神经网络的可靠性。该框架联合优化了准确性、校准和精炼，解决了提高校准会降低精炼的常见问题。RefCal利用一种基于监督对比学习的新颖损失函数来明确促进精炼。在CIFAR-100-LT数据集上的测试中，RefCal在准确性、精炼和预期校准误差方面显著优于现有方法。 AI
影响 通过提高置信度估计来增强DNN的可靠性，可能增加用户对AI系统的信任。
排序理由 该集群包含一篇详细介绍提高深度神经网络可靠性新方法的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2605.23249v1 Announce Type: cross Abstract: Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This has motivated research on calibrated models, wher…
Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This has motivated research on calibrated models, where calibration measures how well a model's predicte…