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.

Ex Input:
This paper describes a new method for reducing the error in a classifier. It uses a weight adjustment update, but includes the very simple rule of either adding or subtracting the adjustment, based on whether the data point is currently larger or smaller than the desired value, and on a pointby-point basis. This gives added flexibility to the convergence procedure, where through a series of transpositions, values far away can continue towards the desired value, whereas values that are originally much closer can oscillate from one side to the other. Tests show that the method can successfully classify some known datasets. It can also work in a batch mode, with reduced training times and can be used as part of a neural network, or classifiers in general. There are also some updates on an earlier wave shape paper.

Ex Output:
A New Oscillating-Error Technique for Classifiers

Ex Input:
We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To generate realistic target images, we employ the real/fake-discriminator in Generative Adversarial Nets [1], but also introduce a novel domain-discriminator to make the generated image relevant to the input image. We verify our model through a challenging task of generating a piece of clothing from an input image of a dressed person. We present a high quality clothing dataset containing the two domains, and succeed in demonstrating decent results.

Ex Output:
Pixel-Level Domain Transfer

Ex Input:
In this paper we propose a special type of aggregation function which generalizes the notion of Ordered Weighted Averaging Function OWA. The resulting functions are called Dynamic Ordered Weighted Averaging Functions DYOWAs. This generalization will be developed in such way that the weight vectors are variables depending on the input vector. Particularly, this operators generalize the aggregation functions: Minimum, Maximum, Arithmetic Mean, Median etc, which are extensively used in image processing. In this field of research two problems are considered: The determination of methods to reduce images and the Preprint submitted to IEEE Transactions on Fuzzy Systems. Federal University of Semi-Arid UFERSA, Pau dos Ferros, RN, Brazil, 59.900-000, antonio.diego@ufersa.edu.br DIMAp, valdigleis@ppgsc.ufrn.br DIMAp, ranyer.lopes@gmail.com DIMAp, bedregal@dimap.ufrn.br Dimap, regivan@dimap.ufrn.br DIMAP: Department of Informatics and Applied Mathematics, Federal University of Rio Grande do Norte UFRN, Natal, RN, Brazil, 59.072-970 1 ar X iv :1 60 1. 03 78 5v 1 [ cs .A I] 1 5 Ja n 20 16 construction of techniques which provide noise reduction. The operators described here are able to be used in both cases. In terms of image reduction we apply the methodology provided in [1]. We use the noise reduction operators obtained here to treat the images obtained in the first part of the paper, thus obtaining images with better quality.

Ex Output:
A Method for Image Reduction Based on a Generalization of Ordered Weighted Averaging Functions