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.

Q: Style transfer usually refers to the task of applying color and texture information from a specific style image to a given content image while preserving the structure of the latter. Here we tackle the more generic problem of semantic style transfer: given two unpaired collections of images, we aim to learn a mapping between the corpus-level style of each collection, while preserving semantic content shared across the two domains. We introduce XGAN ("Cross-GAN"), a dual adversarial autoencoder, which captures a shared representation of the common domain semantic content in an unsupervised way, while jointly learning the domain-to-domain image translations in both directions.   We exploit ideas from the domain adaptation literature and define a semantic consistency loss which encourages the model to preserve semantics in the learned embedding space. We report promising qualitative results for the task of face-to-cartoon translation. The cartoon dataset we collected for this purpose will also be released as a new benchmark for semantic style transfer.

A: XGAN is an unsupervised model for feature-level image-to-image translation applied to semantic style transfer problems such as the face-to-cartoon task, for which we introduce a new dataset.
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Q: In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs). GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer. In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data. Attempts have been made for utilizing GANs with word embeddings for text generation. This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures. The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

A: Generating text using sentence embeddings from Skip-Thought Vectors with the help of Generative Adversarial Networks.
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Q: Recurrent  neural  networks  (RNNs)  are  a  powerful tool for modeling sequential data. Despite their widespread usage, understanding how RNNs solve complex problems remains elusive.   Here, we characterize how popular RNN architectures perform document-level sentiment classification. Despite their theoretical capacity to implement complex, high-dimensional computations, we find that  trained  networks  converge  to  highly  interpretable, low-dimensional representations.   We identify a simple mechanism, integration along an approximate line attractor, and find this mechanism present across RNN architectures (including LSTMs, GRUs, and vanilla RNNs). Overall, these results demonstrate that surprisingly universal and human interpretable computations can arise across a range of recurrent networks.

A:
We analyze recurrent networks trained on sentiment classification, and find that they all exhibit approximate line attractor dynamics when solving this task.
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