Object recognition system employing a sparse comparison neural network

Abstract

A neural network for comparing a known input to an unknown input comprises a first layer for receiving a first known input tensor and a first unknown input tensor. A second layer receives the first known and unknown input tensors. The second layer has at least one first trainable weight tensor associated with the first known input tensor and at least one second trainable weight tensor associated with the first unknown input tensor. The second layer includes at least one first processing element for transforming the first known input tensor on the first trainable weight tensor to produce a first known output and at least one second processing element for transforming the first unknown input tensor on the second trainable weight tensor to produce a first unknown output. The first known output comprises a first known output tensor of at least rank zero and has a third trainable weight tensor associated therewith. The first unknown output comprises a first unknown output tensor of at least rank zero and has a fourth trainable weight tensor associated therewith. The first known output tensor and the first unknown tensor are combined to form a second input tensor. A third layer receives the second input tensor. The third layer has at least one fifth trainable weight tensor associated with the second input tensor. The third layer includes at least one third processing element for transforming the second input tensor on the fifth trainable weight tensor, thereby comparing the first known output with the first unknown output and producing a resultant output. The resultant output is indicative of the degree of similarity between the first known input tensor and the first unknown input tensor.