To understand how object vision develops in infancy and childhood, it will be necessary to develop testable computational models. Deep neural networks (DNNs) have proven valuable as models of adult vision, but it is not yet clear if they have any value as models of development. As a first model, we measured learning in a DNN designed to mimic the architecture and representational geometry of the visual system (CORnet). We quantified the development of explicit object representations at each level of this network through training by freezing the convolutional layers and training an additional linear decoding layer. We evaluate decoding accuracy on the whole ImageNet validation set, and also for individual visual classes. CORnet, however, uses supervised training and because infants have only extremely impoverished access to labels they must instead learn in an unsupervised manner. We therefore also measured learning in a state-of-the-art unsupervised network (DeepCluster). CORnet and DeepCluster differ in both supervision and in the convolutional networks at their heart, thus to isolate the effect of supervision, we ran a control experiment in which we trained the convolutional network from DeepCluster (an AlexNet variant) in a supervised manner. We make predictions on how learning should develop across brain regions in infants. In all three networks, we also tested for a relationship in the order in which infants and machines acquire visual classes, and found only evidence for a counter-intuitive relationship. We discuss the potential reasons for this. DNNs were inspired by the brain. Although DNNs learn like humans from large quantities of data, there is little work to build formal connections between infant and machine learning. Such connections have the potential to bring considerable insight to both fields but the challenge is to find defining characteristics that can be measured in both systems. This paper has addressed this challenge by measuring two characteristic features in DNNs that can be measured in infants. A APPENDIX: DETERMINING NUMBER OF TRAINING EPOCHS FOR THE OBJECT DECODER Training the object decoders was the most computationally expensive part of this project, as one was trained for every layer across many epochs and models. It was therefore necessary to use as few training epochs as possible. To evaluate how many were needed, we trained decoders for 5 epochs on features from a sample of convolutional training epochs (0, 20, 40, 60) and all layers (Fig. 4) . It was found that while there was a steady increase in decoding performance up to (and presumably beyond) the 5 epochs, the relative performance across different layers, or epochs, was broadly captured by epoch 2. For further analyses we therefore used 2 epochs of training for the decoding layer. Fig. 1 showed the layerwise changes in top-5 precision through learning. Fig. 5 shows the corresponding changes in cross-entropy loss.
Unsupervised networks learn from bottom up; machines and infants acquire visual classes in different orders
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