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Convolutional Neural Networks (CNNs) have repeatedly been shown to be the state of the art method for natural signal classification -image classification in particular. Unfortunately, due to the high model complexity CNNs often cannot be used for object detection tasks with real-time constraints, where many predictions have to be made on sub-windows of a large input image. We demonstrate how two recent advances in CNN efficiency can be combined, with modifications, to provide a substantialdoi:10.1145/2683405.2683429 dblp:conf/ivcnz/GoukB14 fatcat:junfq22ybfaazb7xraxszxkl4u