Deep Context-Aware Novelty Detection
This addresses the challenge of dynamic novelty detection in evolving data scenarios, but it is incremental as it builds on existing deep autoencoder methods with context adaptation.
The paper tackles the problem of novelty detection when data distributions change over time or context, proposing a context-aware deep autoencoder approach that uses auxiliary labels to adapt to varying definitions of normal and novel, achieving performance comparable to individually trained models on image and audio data.
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.