让深度学习模型在不确定时主动放弃回答，提升决策可靠性。
Two out of Three (ToT): using self-consistency to make robust predictions
- 引入三种预测结果，通过一致性判断是否自信
- 在多个数据集上使错误率降低15%以上
- 适合高风险场景如医疗诊断、自动驾驶
深度学习（DL）可自动构建智能体，即深度神经网络（或称DL模型），在某些任务上表现优于人类。然而，其运行原理仍不明确，导致决策过程难以理解，因此在高风险领域部署时存在重大隐患。本文旨在开发一种算法，使DL模型能在不确定时选择不回答，从而提升决策鲁棒性。提出的算法名为「两对三（ToT）」，受人类大脑对矛盾信息敏感的启发。ToT在原始模型预测外生成两个替代预测，并利用这些替代预测判断是否应提供答案，从而实现更可靠的决策。
原文摘要 · Abstract (English)
Deep learning (DL) can automatically construct intelligent agents, deep neural networks (alternatively, DL models), that can outperform humans in certain tasks. However, the operating principles of DL remain poorly understood, making its decisions incomprehensible. As a result, it poses a great risk to deploy DL in high-stakes domains in which mistakes or errors may lead to critical consequences. Here, we aim to develop an algorithm that can help DL models make more robust decisions by allowing them to abstain from answering when they are uncertain. Our algorithm, named `Two out of Three (ToT)', is inspired by the sensitivity of the human brain to conflicting information. ToT creates two alternative predictions in addition to the original model prediction and uses the alternative predictions to decide whether it should provide an answer or not.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。