In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
--------
Question: We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image. We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image. Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image. We demonstrate that our approach produces more diverse and plausible colorizations than existing methods, as judged by human raters in a "Visual Turing Test".

Answer: PIXCOLOR: PIXEL RECURSIVE COLORIZATION

Question: This paper describes the realization of the Ontology Web Search Engine. The Ontology Web Search Engine is realizable as independent project and as a part of other projects. The main purpose of this paper is to present the Ontology Web Search Engine realization details as the part of the Semantic Web Expert System and to present the results of the Ontology Web Search Engine functioning. It is expected that the Semantic Web Expert System will be able to process ontologies from the Web, generate rules from these ontologies and develop its knowledge base.

Answer: REALIZATION OF ONTOLOGY WEB SEARCH ENGINE

Question: <lb>We consider the problem of subspace clustering: given points that lie on or near the union of<lb>many low-dimensional linear subspaces, recover the subspaces. To this end, one first identifies<lb>sets of points close to the same subspace and uses the sets to estimate the subspaces. As the<lb>geometric structure of the clusters (linear subspaces) forbids proper performance of general<lb>distance based approaches such as K-means, many model-specific methods have been proposed.<lb>In this paper, we provide new simple and efficient algorithms for this problem. Our statistical<lb>analysis shows that the algorithms are guaranteed exact (perfect) clustering performance under<lb>certain conditions on the number of points and the affinity between subspaces. These conditions<lb>are weaker than those considered in the standard statistical literature. Experimental results on<lb>synthetic data generated from the standard unions of subspaces model demonstrate our theory.<lb>We also show that our algorithm performs competitively against state-of-the-art algorithms on<lb>real-world applications such as motion segmentation and face clustering, with much simpler<lb>implementation and lower computational cost.

Answer:
Greedy Subspace Clustering