The field of quantum computing, especially quantum machine learning (QML), has been the subject of much research in recent years. Leveraging the quantum properties of superposition and entanglement promises exponential decrease in computation costs. With the promises of increased speed and accuracy in the quantum paradigm, many classical machine learning algorithms have been adapted to run on quantum computers, typically using a quantum-classical hybrid model. While some work has been done to compare classical and quantum classification algorithms in the Electro-Optical (EO) image domain, this paper will compare the performance of classical and quantum-hybrid classification algorithms in their applications on Synthetic Aperture Radar (SAR) data using the MSTAR dataset. We find that there is no significant difference in classification performance when training with quantum algorithms in ideal simulators as compared to their classical counterparts. However, the true performance benefits will become more apparent as the hardware matures.
We compare the effectiveness of using a trained-from-scratch, unsupervised deep generative Variational Autoencoder (VAE) model as a solution to generic representation learning problems for Synthetic Aperture Radar (SAR) data as compared to the more common approach of using an Electric Optical (EO) transfer learning method. We find that a simple, unsupervised VAE training framework outperforms an EO transfer learning model at classification.
We introduce a new and efficient solution to the Positive and Unlabeled (PU) problem which is tailored specifically for a deep learning framework. We demonstrate the merit of this method using image classification. When only positive and unlabeled images are available for training, our custom loss function, paired with a simple linear transform of the output, results in an inductive classifier where no estimate of the class prior is required. This algorithm, known as the aaPU (Adaptive Asymmetric Positive Unlabeled) algorithm, provides near supervised classification accuracy with very low levels of labeled data on several image benchmark sets. aaPU demonstrates significant performance improvements over current state-of-the-art positive unlabeled learning algorithms.
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