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Deep Neural Networks trained on academic datasets often fail when applied to the real world. These failures generally arise from unknown inputs that are not of interest to the system. The mis-classification of these unknown inputs as one of the known classes highlights the need for more robust deep networks. The problem of identifying samples that are not of interest to the system has previously been tackled by either thresholding softmax, which by construction cannot return none of the knowndblp:conf/cvpr/DhamijaGB19 fatcat:b35i4nnw3rf4xm67zyonyy4k2q