To understand how the brain and mind develop in infancy, it is necessary to develop testable computational models. In recent years, DNNs have proven valuable as models of the adult brain. In the domain of object recognition, for example, convolutional DNNs currently provide the best way to predict the response to a novel stimulus in the ventral visual stream of the adult monkey and adult human. Given this success in modelling the mature brain, we propose them as candidate models for the learning process. We tested if learning in different DNNs yields distinct trajectories of development at the large scale that could be testable in future brain imaging experiments. First, we quantified the development of explicit object representation at each layer in the network at various points in the learning process, by freezing the convolutional weights and training an additional linear decoding layer. In two DNNs trained in a supervised way (CORnet-S and Alexnet), object representation was strongest in the top layer throughout learning. Infants, however, have only extremely impoverished access to labels, and so unsupervised learning is likely a better model for their learning process. Infants are known to identify clusters of similar-looking objects, and so we evaluated an unsupervised DNN with a clustering objective (DeepCluster). Through learning, there was a bottom-up sweep in the development of object representations, with the penultimate layer strongest at the end. These results show that different training strategies yield different layerwise trajectories of development, which could be empirically measured in infants with neuroimaging. In all three DNNs, we also tested for a relationship in the order in which infants and machines acquire visual classes. We found different patterns, which could be driven by the different visual features that DNNs and humans exploit, or by differences in the objective function used for learning. Our results show that DNNs can make distinct and testable predictions for infant development. The parallel that we are creating highlights ways in which knowledge of the infant brain and mind could guide the design of new DNNs, such as by providing insight into the selection of auxiliary tasks for unsupervised training, from the myriad available.
Summary
The paper aims to understand how object vision develops in infancy and childhood by using deep learning models. In particular, it chooses two deep nets, CORnet and DeepCluster to measure learning. CORnet is supervised and is designed to mimic the architecture and representational geometry of the visual system. It tries to quantify 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. The paper evaluates the decoding accuracy on the whole ImageNet validation set for individual visual classes. DeepCluster differ in both supervision and in the convolutional networks. To isolate the effect of supervision, it ran a control experiment in which the convolutional network from DeepCluster (an AlexNet variant) is trained in a supervised manner. The paper tries to draw conclusions on how learning should develop across brain regions in infants. In all the networks, it 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. 
Limitations
The topic is extremely interesting and worth intense study. However, the approach is not convincing. CORnet may have some relevances. It is not clear how well it models the representational geometry of the visual system. It is even less clear whether DeepCluster is relevant. Why would it be related to infant learning? 
The whole idea of using DNN to infer biological learning is built on shaky ground given how little we know the learning mechanism of the brain. In particular, back propagation is not widely considered possible in biology. Given the learning mechanism may be very different. What is the basis of using DNN to study the infant learning?
The findings are also not very surprising and offer much for the community.
Given the paper lacks rigor and findings, it does not meet the bar of ICLR.