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Generative adversarial network based novelty detection usingminimized reconstruction error Article

Huan-gang WANG, Xin LI, Tao ZHANG

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 1,   Pages 116-125 doi: 10.1631/FITEE.1700786

Abstract: Generative adversarial network (GAN) is the most exciting machine learning breakthrough in recent years

Keywords： Generative adversarial network (GAN)     Novelty detection     Tennessee Eastman (TE) process    

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Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial Special Feature on Intelligent Design

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1632-1643 doi: 10.1631/FITEE.1900399

Abstract: The synthesis module is developed based on a conditional generative adversarial network, which enables

Keywords： Cantonese porcelain     Classification     Generative adversarial network     Creative arts    

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Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Engineering 2023, Volume 25, Issue 6,   Pages 9-11 doi: 10.1016/j.eng.2023.04.004

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Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models Review

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Engineering 2019, Volume 5, Issue 5,   Pages 948-953 doi: 10.1016/j.eng.2019.03.010

Abstract: networks and human visual streams in terms of the architecture and computational rules Furthermore, deep generativemodels (e.g., variational autoencoders (VAEs) and generative adversarial networks (GANs)) have produced

Keywords： Brain encoding and decoding     Functional magnetic resonance imaging     Deep neural networks     Deep generative    

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Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Frontiers in Energy 2023, Volume 17, Issue 2,   Pages 211-227 doi: 10.1007/s11708-022-0847-3

Abstract: interior point methods with adversarial networks (Modified IPMAN) as core modules, and discriminator generative

Keywords： end-to-end learning     microgrids     voltage profile improvement     generative adversarial network    

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Latent source-specific generative factor learning for monaural speech separation using weighted-factor

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 11,   Pages 1535-1670 doi: 10.1631/FITEE.2000019

Abstract: Much recent progress in monaural (MSS) has been achieved through a series of architectures based on s, which use an encoder to condense the input signal into compressed features and then feed these features into a decoder to construct a specific audio source of interest. However, these approaches can neither learn of the original input for MSS nor construct each audio source in mixed speech. In this study, we propose a novel weighted-factor (WFAE) model for MSS, which introduces a regularization loss in the objective function to isolate one source without containing other sources. By incorporating a latent attention mechanism and a supervised source constructor in the separation layer, WFAE can learn source-specific and a set of discriminative features for each source, leading to MSS performance improvement. Experiments on benchmark datasets show that our approach outperforms the existing methods. In terms of three important metrics, WFAE has great success on a relatively challenging MSS case, i.e., speaker-independent MSS.

Keywords： 语音分离；生成因子；自动编码器；深度学习    

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SmartPaint: a co-creative drawing system based on generative adversarial networks Special Feature on Intelligent Design

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 12,   Pages 1644-1656 doi: 10.1631/FITEE.1900386

Abstract: To bridge this gap, we have developed SmartPaint, a co-creative drawing system based on generative adversarial

Keywords： Co-creative drawing     Deep learning     Image generation    

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A Geometric Understanding of Deep Learning Article

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Engineering 2020, Volume 6, Issue 3,   Pages 361-374 doi: 10.1016/j.eng.2019.09.010

Abstract: font-family:"Times New Roman",serif">This work introduces an optimal transportation (OT) view of generativeWe also propose a novel generative model, which uses an autoencoder (AE) for manifold learning and OT

Keywords： Generative     Adversarial     Deep learning     Optimal transportation     Mode collapse    

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Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology Article

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Engineering 2019, Volume 5, Issue 1,   Pages 156-163 doi: 10.1016/j.eng.2018.11.018

Abstract: In this paper, we propose an approach based on a generative adversarial network (GAN) combined with a

Keywords： Artificial intelligence     Generative adversarial network     Deep neural network     Small sample size     Cancer    

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Title Author Date Type Operation

Generative adversarial network based novelty detection usingminimized reconstruction error

Huan-gang WANG, Xin LI, Tao ZHANG

Journal Article

Cantonese porcelain classification and image synthesis by ensemble learning and generative adversarial

Steven Szu-Chi CHEN, Hui CUI, Ming-han DU, Tie-ming FU, Xiao-hong SUN, Yi JI, Henry DUH

Journal Article

Surprising Advances in Generative Artificial Intelligence Prompt Amazement—and Worries

Dana Mackenzie

Journal Article

Brain Encoding and Decoding in fMRI with Bidirectional Deep Generative Models

Changde Du, Jinpeng Li, Lijie Huang, Huiguang He

Journal Article

Topology-independent end-to-end learning model for improving the voltage profile in microgrids-integrated power distribution networks

Journal Article

Latent source-specific generative factor learning for monaural speech separation using weighted-factor

Jing-jing Chen, Qi-rong Mao, You-cai Qin, Shuang-qing Qian, Zhi-shen Zheng,2221808071@stmail.ujs.edu.cn,mao_qr@ujs.edu.cn,2211908026@stmail.ujs.edu.cn,2211908025@stmail.ujs.edu.cn,3160602062@stmail.ujs.edu.cn

Journal Article

SmartPaint: a co-creative drawing system based on generative adversarial networks

Lingyun SUN, Pei CHEN, Wei XIANG, Peng CHEN, Wei-yue GAO, Ke-jun ZHANG

Journal Article

A Geometric Understanding of Deep Learning

Na Lei, Dongsheng An, Yang Guo, Kehua Su, Shixia Liu, Zhongxuan Luo, Shing-Tung Yau, Xianfeng Gu

Journal Article

Wasserstein GAN-Based Small-Sample Augmentation for New-Generation Artificial Intelligence: A Case Study of Cancer-Staging Data in Biology

Yufei Liu, Yuan Zhou, Xin Liu, Fang Dong, Chang Wang, Zihong Wang

Journal Article