  We quantify the generalization of a convolutional neural network (CNN)
trained to identify cars. First, we perform a series of experiments to train
the network using one image dataset - either synthetic or from a camera - and
then test on a different image dataset. We show that generalization between
images obtained with different cameras is roughly the same as generalization
between images from a camera and ray-traced multispectral synthetic images.
Second, we use ISETAuto, a soft prototyping tool that creates ray-traced
multispectral simulations of camera images, to simulate sensor images with a
range of pixel sizes, color filters, acquisition and post-acquisition
processing. These experiments reveal how variations in specific camera
parameters and image processing operations impact CNN generalization. We find
that (a) pixel size impacts generalization, (b) demosaicking substantially
impacts performance and generalization for shallow (8-bit) bit-depths but not
deeper ones (10-bit), and (c) the network performs well using raw (not
demosaicked) sensor data for 10-bit pixels.
