Unsupervised learning, Image classification, Image Represented by a Fuzzy Function, IRFF, Autoencoder

We are focusing on image classification in industrial processing taking into account the most problematic issue of the processing: the lack of labeled data. Here, we are considering three datasets: the first one is an unsorted collection of all types of manufactured products and includes 100 images per class. The second one consists of products sorted into particular classes by a specialized employee and includes only ten images per class. The last one includes a massive volume of labeled images, but it is used only for the proposal validation. As the configuration is challenging for neural networks, we propose to use Image Represented by a Fuzzy Function in order to enrich original image information. We solve the task using various autoencoder architectures and prove that such the proposal increases the autoencoders success rate.