论文标题
深层背景感知新颖性检测
Deep Context-Aware Novelty Detection
论文作者
论文摘要
新颖性检测的一个常见假设是，“正常”和“新颖”数据的分布都是静态的。但是，这种情况通常不是这种情况 - 例如，随着时间的推移和新颖的定义取决于上下文信息，数据随着时间的流逝而演变，或者都会导致这些分布的变化。在尝试在一个方案中正常数据的分布与在另一种情况下的新数据相似的数据集上，这可能会带来重大困难。在本文中，我们建议对深层自动编码器的新颖性检测方法来解决这些困难。我们创建了一个半监督的网络体系结构，该架构利用辅助标签来揭示上下文信息，并允许模型适应正常和新颖变化的定义的各种上下文。我们在图像数据和现实世界的音频数据上评估了我们的方法，以显示这些特征，并表明可以在单个模型中实现单独训练的模型的性能。
A common assumption of novelty detection is that the distribution of both "normal" and "novel" data are static. This, however, is often not the case - for example scenarios where data evolves over time or scenarios in which the definition of normal and novel depends on contextual information, both leading to changes in these distributions. This can lead to significant difficulties when attempting to train a model on datasets where the distribution of normal data in one scenario is similar to that of novel data in another scenario. In this paper we propose a context-aware approach to novelty detection for deep autoencoders to address these difficulties. We create a semi-supervised network architecture that utilises auxiliary labels to reveal contextual information and allow the model to adapt to a variety of contexts in which the definitions of normal and novel change. We evaluate our approach on both image data and real world audio data displaying these characteristics and show that the performance of individually trained models can be achieved in a single model.
