When seeing a new object, humans can immediately recognize it across different retinal locations: the internal object representation is invariant to translation. It is commonly believed that Convolutional Neural Networks (CNNs) are architecturally invariant to translation thanks to the convolution and/or pooling operations they are endowed with. In fact, several studies have found that these networks systematically fail to recognise new objects on untrained locations. In this work, we test a wide variety of CNNs architectures showing how, apart from DenseNet-121, none of the models tested was architecturally invariant to translation. Nevertheless, all of them could learn to be invariant to 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 are fully translated across the input canvas. At the same time, this invariance can be disrupted by further training due to catastrophic forgetting/interference. These experiments show how pretraining a network on an environment with the right `latent' characteristics (a more naturalistic environment) can result in the network learning deep perceptual rules which would dramatically improve subsequent generalization.

翻译：当看到一个新对象时, 人类可以立即在不同的视网膜位置中识别它: 内部对象表示是无法翻译的。 一般认为, 进化神经网络( CNNs) 在结构上是无法翻译的。 事实上, 一些研究发现, 这些网络在未经训练的地点系统地无法识别新对象。 在这项工作中, 我们测试了各种各样的CNN结构结构, 显示除了DenseNet-121之外, 所测试的模型中没有一个在结构上无法翻译。 然而, 它们都可以学会不翻译。 我们展示了如何通过在图像网络上进行预培训来做到这一点, 当所有项目都完全翻译在输入轨图上时, 有时可以使用简单得多的数据集。 同时, 这种差异可以通过灾难性的遗忘/ 干扰的进一步培训来中断。 这些实验表明, 如何预先训练一个环境的网络, 其右面的“ 相对性” 特性( 比较自然环境) 能够导致网络学习深层次的认知规则, 从而大大地改进随后的一般化。