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When seeing a new object, humans can immediately recognize it acrossdifferent retinal locations: the internal object representation is invariant totranslation. It is commonly believed that Convolutional Neural Networks (CNNs)are architecturally invariant to translation thanks to the convolution and/orpooling operations they are endowed with. In fact, several studies have foundthat these networks systematically fail to recognise new objects on untrainedlocations. In this work, we test a wide variety of CNNs architectures showinghow, apart from DenseNet-121, none of the models tested was architecturallyinvariant to translation. Nevertheless, all of them could learn to be invariantto translation. We show how this can be achieved by pretraining on ImageNet,and it is sometimes possible with much simpler data sets when all the items arefully translated across the input canvas. At the same time, this invariance canbe disrupted by further training due to catastrophic forgetting/interference.These experiments show how pretraining a network on an environment with theright `latent' characteristics (a more naturalistic environment) can result inthe network learning deep perceptual rules which would dramatically improvesubsequent generalization.

Convolutional Neural Networks Are Not Invariant to Translation, but They Can Learn to Be