Hybrid Semi-Parallel Deep Neural Networks (SPDNN) – example methodologies and use cases
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Corcoran, Peter, & Bazrafkkan, Shabab. (2018). Hybrid Semi-Parallel Deep Neural Networks (SPDNN) – example methodologies and use cases. Paper presented at the Embedded Vision Summit, Santa Clara Convention Center, Silicon Valley, Santa Clara, California, 22-23 May.
Deep neural networks (DNNs) are typically trained on specific datasets, optimized with particular discriminating capabilities. Often several different DNN topologies are developed solving closely related aspects of a computer vision problem. But to utilize these topologies together, leveraging their individual discriminating capabilities, requires implementing each DNN separately, increasing the cost of practical solutions. In this talk, a methodology to merge multiple deep networks using graph contraction is developed. This provides a single network topology, achieving significant reduction in size over the individual networks. More significantly, this merged SPDNN network can be re-trained across the combined datasets used to train the original networks, improving its accuracy over the original networks. The result is a single network that is more generic, but with equivalent – or often enhanced – performance over a wider range of input data. Examples of several problems in contemporary computer vision are solved using SPDNNs. These include significantly improving segmentation accuracy of eye-iris regions (a key component of iris biometric authentication) and mapping depth from monocular images, demonstrating equivalent performance to stereo depth mapping.