instruction:
In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
question:
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.
answer:
Unsupervised networks learn from bottom up; machines and infants acquire visual classes in different orders


question:
Modeling informal inference in natural language is very challenging. With the recent availability of large annotated data, it has become feasible to train complex models such as neural networks to perform natural language inference (NLI), which have achieved state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform NLI from the data? If not, how can NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we aim to answer these questions by enriching the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models with external knowledge further improve the state of the art on the Stanford Natural Language Inference (SNLI) dataset.
answer:
the proposed models with external knowledge further improve the state of the art on the SNLI dataset.


question:
Unsupervised image-to-image translation aims to learn a mapping between several visual domains by using unpaired training pairs. Recent studies have shown remarkable success in image-to-image translation for multiple domains but they suffer from two main limitations: they are either built from several two-domain mappings that are required to be learned independently and/or they generate low-diversity results, a phenomenon known as model collapse. To overcome these limitations, we propose a method named GMM-UNIT based on a content-attribute disentangled representation, where the attribute space is fitted with a GMM. Each GMM component represents a domain, and this simple assumption has two prominent advantages. First, the dimension of the attribute space does not grow linearly with the number of domains, as it is the case in the literature. Second, the continuous domain encoding allows for interpolation between domains and for extrapolation to unseen domains. Additionally, we show how GMM-UNIT can be constrained down to different methods in the literature, meaning that GMM-UNIT is a unifying framework for unsupervised image-to-image translation.
answer:

GMM-UNIT is an image-to-image translation model that maps an image to multiple domains in a stochastic fashion.