Self-Supervised Auto-Encoding Multi-Transformations for Airplane Classification.

In this paper, we present a self-supervised learning method of Auto-Encoding Multi-Transformations (AEMT) for airplane classification. In this method, the image features are learned in an unsupervised way by simultaneously estimating multiple image transformations from the features of original and transformed images instead of reconstructing the input images. Besides, we propose two structure variants of the AEMT method: composite and parallel modes of which the former transforms the images in a composite fashion while the latter does it in parallel. The experimental results demonstrate that the proposed method outperforms the state-of-the-art self-supervised learning methods for the airplane classification task.