Abstract: A computer-implemented method includes training at least a generative adversarial network, the method operable on one or more processors. The method includes at least (1) applying pattern extraction to a set of training data to extract one or more feature embeddings representing one or more features of the training data, (2) attenuating the one or more feature embeddings to create one or more attenuated feature embeddings, (3) providing the one or more attenuated embeddings to a generator of the generative adversarial network as a condition to at least partly control the generator in generating synthetic data, the providing being performed automatically and dynamically during training of the generator, and (4) with the generator, generating synthetic data based at least in part on the attenuated embeddings.

Abstract: In some embodiments, an edge device is configured to execute machine learning procedures with a sparse dataset. The edge device includes at least (1) one or more sensor interfaces, (2) one or more microcontrollers (MCUs), and one or more memories in communication with the one or more microcontrollers. The one or more memories contain one or more executable instructions that cause the one or more microcontrollers to perform operations that include at least: (a) receiving one or more batches of real-time sensor data via the one or more sensor interfaces, the one or more batches defining the sparse dataset, and creating one or more batches of augmented data with the one or more batches of real-time sensor data and one or more batches of generated synthetic data. In some embodiments the edge device is a resource-constrained edge device.