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We explore the question of how the resolution of the input image ("inputresolution") affects the performance of a neural network when compared to theresolution of the hidden layers ("internal resolution"). Adjusting thesecharacteristics is frequently used as a hyperparameter providing a trade-offbetween model performance and accuracy. An intuitive interpretation is that thereduced information content in the low-resolution input causes decay in theaccuracy. In this paper, we show that up to a point, the input resolution aloneplays little role in the network performance, and it is the internal resolutionthat is the critical driver of model quality. We then build on these insightsto develop novel neural network architectures that we call \emph{IsometricNeural Networks}. These models maintain a fixed internal resolution throughouttheir entire depth. We demonstrate that they lead to high accuracy models withlow activation footprint and parameter count.

Non-discriminative data or weak model? On the relative importance of data and model resolution