Abstract: Described is a system for detecting and classifying new patterns of objects and images for applications where labeled data is scarce. In operation, the system trains a neural network with unlabeled images and extracts features with the neural network from both the unlabeled images and a set of labeled images to generate a feature space. Labels are propagated in the feature space using nearest neighbors, allowing for modeling of a per-class simplified distribution. An object in a new test image can then be classified using reconstruction error based on the per-class simplified distributions.

Abstract: Described is a system for pattern recognition and graph extraction. The system operates by obtaining data from a quantum dot device having gate defined semiconductor quantum dots. An image with pixel-level coordinates is then generated from the data. Ground truth graph annotations are received of the image. A deep network processes the image and pixel-level coordinates to generate a predicted graph. The predicted graph is compared to the graph annotations to generate a loss, which allows the deep network to be optimized by updating parameters in the deep network based on the loss. The image with pixel-level coordinates is then used to generate an optimized predicted graph, which allows for identifying operational voltages to apply to the quantum dot device.