This paper focuses on the problem of detecting out-of-distribution (ood) samples with neural nets. In image recognition tasks, the trained classifier often gives high confidence score for input images which are remote from the in-distribution (id) data, and this has greatly limited its application in real world. For alleviating this problem, we propose a GAN based boundary aware classifier (GBAC) for generating a closed hyperspace which only contains most id data. Our method is based on the fact that the traditional neural net seperates the feature space as several unclosed regions which are not suitable for ood detection. With GBAC as an auxiliary module, the ood data distributed outside the closed hyperspace will be assigned with much lower score, allowing more effective ood detection while maintaining the classification performance. Moreover, we present a fast sampling method for generating hard ood representations which lie on the boundary of pre-mentioned closed hyperspace. Experiments taken on several datasets and neural net architectures promise the effectiveness of GBAC.
翻译:本文侧重于检测使用神经网的分布( 散装) 样本的问题。 在图像识别任务中, 训练有素的分类器通常对远离分布( id) 数据的输入图像给予高度信任分数, 这极大地限制了其在真实世界中的应用。 为了缓解这一问题, 我们建议使用基于 GAN 的边界感知分类器( GBAC ) 来生成一个只包含大多数 id 数据的封闭性超空间。 我们的方法基于以下事实: 传统神经网将地物空间作为若干不适于检测的未封闭区域隔绝。 以 GBAC 为辅助模块, 在封闭性超空域外传播的光学数据将被分配得分要低得多, 使得在保持分类性能的同时能够更有效地检测。 此外, 我们提出了一个快速的取样方法, 用于生成硬体体体体积的表达方式, 位于先前提到的封闭性超空空间的边界上。 在几个数据集和神经网结构上进行的实验将保证 GBAC 的有效性 。