Convolutional autoencoder-based multimodal one-class classification
Firas Laakom Fahad Sohrab Jenni Raitoharju Alexandros Iosifidis Moncef Gabbouj
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.