Self-Supervised Learning for Specified Latent Representation

Current latent representation methods using unsupervised learning have no semantic meaning; thus, it is difficult to directly express their physical task in the real world. To this end, this paper attempts to propose a specified latent representation with physical semantic meaning. First, a few labeled samples are used to generate the framework of the latent space, and these labeled samples are mapped to framework nodes in the latent space. Second, a self-learning method using structured unlabeled samples is proposed to shape the free space between the framework nodes in the latent space. The proposed specified latent representation therefore possesses the advantages provided by both supervised and unsupervised learning. The proposed method is verified by numerical simulations and real-world experiments.