Representational Learning for Sonar ATR

Learned representations have been shown to give hopeful results for solving a multitude of novel learning tasks, even though these tasks may be unknown when the model is being trained. A few notable examples include the techniques of topic models, deep belief networks, deep Boltzmann machines, and local discriminative Gaussians, all inspired by human learning. This self-learning of new concepts via rich generative models has emerged as a promising area of research in machine learning. Although there has been recent progress, existing computational models are still far from being able to represent, identify and learn the wide variety of possible patterns and structure in real-world data. An important issue for further consideration is the use of unsupervised representations for novel underwater target recognition applications. This work will discuss and demonstrate the use of latent Dirichlet allocation and autoencoders for learning unsupervised representations of objects in sonar imagery. The objective is to make these representations more abstract and invariant to noise in the training distribution and improve performance.