The increased success of Deep Learning (DL) has recently sparked large-scale deployment of DL models in many diverse industry segments. Yet, a crucial weakness of supervised model is the inherent difficulty in handling out-of-distribution samples, i.e., samples belonging to classes that were not presented to the model at training time. We propose in this paper a novel way to formulate the out-of-distribution detection problem, tailored for DL models. Our method does not require fine tuning process on training data, yet is significantly more accurate than the state of the art for out-of-distribution detection.
翻译：深层学习(DL)的日益成功最近引发了在许多不同行业部门大规模部署DL模型,然而,受监督模式的一个关键弱点是处理分配外样本的内在困难,即属于培训时没有提交模式的各类样本。我们在本文件中提出了一种新颖的方法,根据DL模型来制定分配外检测问题。我们的方法不需要对培训数据进行微调,但比在分配外检测方面的最新水平要准确得多。
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