SupplementaryMaterial OnNumerosityofDeepNeuralNetworks 1 Generalizationstudyonobjectdensity

Here we add the generalization results of the Nu-Net when the object density is outside of the distribution of the training set. Specifically, we run the Nu-Net on test images that are the same as the training images but have 50% greatervariations inobject density. The 85% estimation interval length for each input number is shown in Figure 1. It can be seen that, fornumbers 1,2and4,the85% estimation intervallength is1,meaning thattheNu-Net performs very well on small numbers, i.e., on the task of subitizing.