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.

Ex Input:
We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other. Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably. We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance. Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.

Ex Output:
The notion of order learning is proposed and it is applied to regression problems in computer vision


Ex Input:
Deep generative models have achieved impressive success in recent years. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), as powerful frameworks for deep generative model learning, have largely been considered as two distinct paradigms and received extensive independent studies respectively. This paper aims to establish formal connections between GANs and VAEs through a new formulation of them. We interpret sample generation in GANs as performing posterior inference, and show that GANs and VAEs involve minimizing KL divergences of respective posterior and inference distributions with opposite directions, extending the two learning phases of classic wake-sleep algorithm, respectively. The unified view provides a powerful tool to analyze a diverse set of existing model variants, and enables to transfer techniques across research lines in a principled way. For example, we apply the importance weighting method in VAE literatures for improved GAN learning, and enhance VAEs with an adversarial mechanism that leverages generated samples. Experiments show generality and effectiveness of the transfered techniques.

Ex Output:
A unified statistical view of the broad class of deep generative models 


Ex Input:
Place and grid-cells are known to aid navigation in animals and humans. Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space. We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons. Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks. Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.

Ex Output:

We investigated if simple deep networks possess grid cell-like artificial neurons while memory retrieval in the learned concept space.