  Compressed domain image classification performs classification directly on
compressive measurements acquired from the single-pixel camera, bypassing the
image reconstruction step. It is of great importance for extending high-speed
object detection and classification beyond the visible spectrum in a
cost-effective manner especially for resource-limited platforms. Previous
neural network methods require training a dedicated neural network for each
different measurement rate (MR), which is costly in computation and storage. In
this work, we develop an efficient training scheme that provides a neural
network with dynamic-rate property, where a single neural network is capable of
classifying over any MR within the range of interest with a given sensing
matrix. This training scheme uses only a few selected MRs for training and the
trained neural network is valid over the full range of MRs of interest. We
demonstrate the performance of the dynamic-rate neural network on datasets of
MNIST, CIFAR-10, Fashion-MNIST, COIL-100, and show that it generates
approximately equal performance at each MR as that of a single-rate neural
network valid only for one MR. Robustness to noise of the dynamic-rate model is
also demonstrated. The dynamic-rate training scheme can be regarded as a
general approach compatible with different types of sensing matrices, various
neural network architectures, and is a valuable step towards wider adoption of
compressive inference techniques and other compressive sensing related tasks
via neural networks.
