2 papers
cs.CV2020
Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved MobileNets
Daniel Haase, Manuel Amthor
We introduce blueprint separable convolutions (BSConv) as highly efficient building blocks for CNNs. They are motivated by quantitative analyses of kernel properties from trained m…
cs.CV2016
Impatient DNNs - Deep Neural Networks with Dynamic Time Budgets
Manuel Amthor, Erik Rodner, Joachim Denzler
We propose Impatient Deep Neural Networks (DNNs) which deal with dynamic time budgets during application. They allow for individual budgets given a priori for each test example and…