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20172026
most citedData Augmentation in Emotion Classification Using Generative Adversarial Networks

78 citations · 95 across the 13 of their papers we have counts for

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cs.CV2017★ 78 cited

Data Augmentation in Emotion Classification Using Generative Adversarial Networks

Xinyue Zhu, Yifan Liu, Zengchang Qin +1

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Em…

cs.CL2017

Text Generation Based on Generative Adversarial Nets with Latent Variable

Heng Wang, Zengchang Qin, Tao Wan

In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with ge…

cs.SI2017

Motif Iteration Model for Network Representation

Lintao Lv, Zengchang Qin, Tao Wan

Social media mining has become one of the most popular research areas in Big Data with the explosion of social networking information from Facebook, Twitter, LinkedIn, Weibo and so…

cs.CL2017

Logical Parsing from Natural Language Based on a Neural Translation Model

Liang Li, Pengyu Li, Yifan Liu +2

Semantic parsing has emerged as a significant and powerful paradigm for natural language interface and question answering systems. Traditional methods of building a semantic parser…

cs.CV2017

Auto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks

Yifan Liu, Zengchang Qin, Zhenbo Luo +1

Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learni…

cs.SI2017

Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

Yifan Liu, Zengchang Qin, Pengyu Li +1

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sent…