One-class classification refers to approaches of learning using data from a single class only. In this paper, we propose a deep learning one-class classification method suitable for multimodal data, which relies on two convolutional autoencoders jointly trained to reconstruct the positive input data while obtaining the data representations in the latent space as compact as possible. During inference, the distance of the latent representation of an input to the origin can be used as an anomaly score. Experimental results using a multimodal macroinvertebrate image classification dataset show that the proposed multimodal method yields better results as compared to the unimodal approach. Furthermore, study the effect of different input image sizes, and we investigate how recently proposed feature diversity regularizers affect the performance of our approach. We show that such regularizers improve performance.

本文提出了一种适用于多模态数据的深度学习一类分类方法，通过联合训练两个卷积自编码器，使其在重构正样本输入数据的同时，在潜在空间中获得尽可能紧凑的数据表示。在推理过程中，可以使用输入的潜在表示与原点的距离作为异常分数。实验结果表明，与单模态方法相比，所提出的多模态方法在多模态昆虫图像分类数据集上获得了更好的结果。此外，本文还研究了不同输入图像尺寸的影响，并探讨了最近提出的特征多样性正则化对我们方法性能的影响。结果表明，这样的正则化手段能够提高性能。

基于卷积自编码器的多模态一类分类