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The evolution of high performance computing technologies has enabled the large-scale implementation of neuromorphic models and pushed the research in computational intelligence into a new era. Among the machine learning applications, unsupervised detection of anomalous streams is especially challenging due to the requirements of detection accuracy and real-time performance. Designing a computing framework that harnesses the growing computing power of the multicore systems while maintaining high sensitivity and specificity to the anomalies is an urgent research topic. In this paper, we propose anomaly recognition and detection (AnRAD), a bioinspired detection framework that performs probabilistic inferences. We analyze the feature dependency and develop a self-structuring method that learns an efficient confabulation network using unlabeled data. This network is capable of fast incremental learning, which continuously refines the knowledge base using streaming data. Compared with several existing anomaly detection approaches, our method provides competitive detection quality. Furthermore, we exploit the massive parallel structure of the AnRAD framework. Our implementations of the detection algorithm on the graphic processing unit and the Xeon Phi coprocessor both obtain substantial speedups over the sequential implementation on general-purpose microprocessor. The framework provides real-time service to concurrent data streams within diversified knowledge contexts, and can be applied to large problems with multiple local patterns. Experimental results demonstrate high computing performance and memory efficiency. For vehicle behavior detection, the framework is able to monitor up to 16000 vehicles (data streams) and their interactions in real time with a single commodity coprocessor, and uses less than 0.2 ms for one testing subject. Finally, the detection network is ported to our spiking neural network simulator to show the potential of adapting to the emerging neuromorphic architectures.

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Recognition systems in the remote sensing domain often operate in "open-world" environments, where they must be capable of accurately classifying data from the indistribution categories while simultaneously detecting and rejecting anomalous/out-of-distribution (OOD) inputs. However, most modern designs use Deep Neural Networks (DNNs) to perform this recognition function that are trained under "closedworld" assumptions in offline-only environments. As a result, by construction these systems are ill-posed to handle anomalous inputs and have no mechanism for improving OOD detection abilities during deployment. In this work, we address these weaknesses from two aspects. First, we introduce advanced DNN training methods to co-design for accuracy and OOD detection in the offline training phase. We then propose a novel "learn-online" workflow for updating the DNNs during deployment using a small library of carefully collected samples from the operating environment. To show the efficacy of our methods we consider experimenting with two popular recognition tasks in remote sensing: scene classification in electro-optical satellite images and automatic target recognition in synthetic aperture radar imagery. In both, we find that our two primary design contributions can individually improve detection performance, while also being complementary. Additionally, we find that detection performance on difficult and highly-granular OOD samples can be drastically improved using only tens or hundreds of samples collected from the environment. Finally, through analysis we determine that the logic for adding/removing samples from the collection library is of key importance and using a proper learning rate during the model update step is critical.

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