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.

翻译：我们用新的评价框架来评价自我监督学习技术(SSL)在分配外的检测性能,与以前的评估方法不同,拟议框架调整OOD样本与分配内样本的距离,我们用模拟样本、图像和文字对三个不同实施拟议框架的OOD检测性能进行广泛综合评估。 SSL方法一致地展示了所有评估环境中OOD检测性能的改善。