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:
Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods. However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information. This causes problems for further performance improvement. In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning. Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB). They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details. Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation. Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods. Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.
answer:
A state-of-the-art model based on global reasoning for image super-resolution

question:
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks. DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them. We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters.
answer:
We propose a novel deep network architecture that can dynamically decide its network capacity as it trains on a lifelong learning scenario.

question:
We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies : Socially Guided Intrinsic Motivation with Procedure Babbling (SGIM-PB). The learner can generalize over its experience to continuously learn new outcomes, by choosing actively what and how to learn guided by empirical measures of its own progress. In this paper, we are considering the learning of a set of interrelated complex outcomes hierarchically organized.

We introduce a new framework called "procedures", which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly more complex motor policies (combinations of primitive motor policies). Our architecture can actively decide which outcome to focus on and which exploration strategy to apply. Those strategies could be autonomous exploration, or active social guidance, where it relies on the expertise of a human teacher providing demonstrations at the learner's request. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand. We also show that our "procedures" increases the agent's capability to learn complex tasks.
answer:
The paper describes a strategic intrinsically motivated learning algorithm which tackles the learning of complex motor policies.