Latent Feature Separation and Extraction with Multiple Parallel Encoders for Convolutional Autoencoder - Kim, Jaehyun; Kim, Myungjun; Shin, Hyunjung (Proceedings - 2022 IEEE International Conference on Big Data and Smart Computing, BigComp 2022, 2022)
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.
Keywords: Auto encoders; Convolutional autoencoder; Correlation coefficient; Features extraction; Image clustering; Image data; Independence; Multi-head convolutional autoencoder; Multiple parallel encoder; Convolutional Autoencoder; correlation coefficient; feature extraction; image clustering; independence; Multi-head Convolutional Autoencoder; multiple parallel encoders