论文标题
评估在可控环境中自我监督学习的分布外检测性能
Evaluation of Out-of-Distribution Detection Performance of Self-Supervised Learning in a Controllable Environment
论文作者
论文摘要
我们使用新的评估框架评估了自我监督学习（SSL）技术（SSL）技术的分布（OOD）检测性能。与先前的评估方法不同，所提出的框架调整了OOD样品与分布样品的距离。我们使用模拟示例，图像和文本评估了在提出的框架的三个不同实现上，评估了OOD检测算法的广泛组合。 SSL方法始终在所有评估设置中始终证明了OOD检测性能的改进。
We evaluate the out-of-distribution (OOD) detection performance of self-supervised learning (SSL) techniques with a new evaluation framework. Unlike the previous evaluation methods, the proposed framework adjusts the distance of OOD samples from the in-distribution samples. We evaluate an extensive combination of OOD detection algorithms on three different implementations of the proposed framework using simulated samples, images, and text. SSL methods consistently demonstrated the improved OOD detection performance in all evaluation settings.
