  High image resolution is critical to obtain a good performance in many
computer vision applications. Computational complexity of CNNs, however, grows
significantly with the increase in input image size. Here, we show that it is
almost always possible to modify a network such that it achieves higher
accuracy at a higher input resolution while having the same number of
parameters or/and FLOPS. The idea is similar to the EfficientNet paper but
instead of optimizing network width, depth and resolution simultaneously, here
we focus only on input resolution. This makes the search space much smaller
which is more suitable for low computational budget regimes. More importantly,
by controlling for the number of model parameters (and hence model capacity),
we show that the additional benefit in accuracy is indeed due to the higher
input resolution. Preliminary empirical investigation over MNIST, Fashion
MNIST, and CIFAR10 datasets demonstrates the efficiency of the proposed
approach.
