We chose MNIST to show that rotated embeddings can occur after convolutional encoders because the data set is simple and has multiple representations for each image. Three representations of the MNIST 36 data set of handwritten digits were considered: the images, grid-like graphs constructed from the images (MNISTSuperpixel 37 ), and sequences from the order of pixels that are drawn (MNISTStroke ). In this study, the task of all data sets was to identify digits below five as the positive class. For the data sets, a CNN, a GNN, and an LSTM were used as convolutional encoders, respectively.