基于深度度量学习与合成离群点暴露的分布外检测
Deep Metric Learning-Based Out-of-Distribution Detection with Synthetic Outlier Exposure
AI总结：
本文提出结合深度度量学习与扩散模型生成合成离群点数据的方法，通过标签混合策略训练OOD检测模型，实验表明其性能优于softmax及强基线。
摘要：
本文提出了一种新方法，将深度度量学习与基于扩散模型的合成数据生成相结合，用于分布外（OOD）检测。离群点暴露是OOD检测中一种流行的方法，模型使用分布内（ID）样本和“已见”OOD样本的混合数据进行训练。对于OOD样本，模型被训练为在正确分类分布内（ID）数据的同时，最小化输出概率与均匀分布之间的KL散度。本文提出了一种标签混合（label-mixup）方法，利用去噪扩散概率模型（DDPMs）生成合成OOD数据。此外，我们还探索了度量学习的最新进展来训练模型。在实验中，我们发现基于度量学习的损失函数表现优于softmax。此外，基线模型（包括softmax和度量学习）在使用生成的OOD数据训练后表现出显著改进。我们的方法在常规OOD检测指标上优于强基线。
英文摘要：
In this paper, we present a novel approach that combines deep metric learning and synthetic data generation using diffusion models for out-of-distribution (OOD) detection. One popular approach for OOD detection is outlier exposure, where models are trained using a mixture of in-distribution (ID) samples and ``seen" OOD samples. For the OOD samples, the model is trained to minimize the KL divergence between the output probability and the uniform distribution while correctly classifying the in-distribution (ID) data. In this paper, we propose a label-mixup approach to generate synthetic OOD data using Denoising Diffusion Probabilistic Models (DDPMs). Additionally, we explore recent advancements in metric learning to train our models. In the experiments, we found that metric learning-based loss functions perform better than the softmax. Furthermore, the baseline models (including softmax, and metric learning) show a significant improvement when trained with the generated OOD data. Our approach outperforms strong baselines in conventional OOD detection metrics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
