Latent Feature Separation and Extraction with Multiple Parallel Encoders for Convolutional Autoencoder

Jaehyun Kim, Myungjun Kim, Hyunjung Shin

Abstract: Much of the real-world image data is unlabeled or mislabeled. Therefore, even if there is no label, if similar images can be grouped together with image data itself and used the group as a label, more image data can be effectively used in various tasks. This will be especially effective when dividing images belonging to the same domain into sub-groups. Therefore, in this study, we propose an image feature extraction method to be used for image clustering. The proposed feature extraction model is the Multi-head Convolutional Autoencoder (MCAE), which is a model composed of multiple encoders in parallel based on the Convolutional Autoencoder (CAE). The proposed model showed about 14% lower test reconstruction loss compared to CAE, and the correlation coefficient between extracted features was about 56% lower. In addition, as the results of clustering based on the extracted features, MCAE-based clustering showed about 3.5 times higher silhouette score than that CAE-based clustering.