Search Results for author: Jiasong Wu

Found 16 papers, 0 papers with code

Generative networks as inverse problems with fractional wavelet scattering networks

no code implementations28 Jul 2020 Jiasong Wu, Jing Zhang, Fuzhi Wu, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu

In order to solve or alleviate the synchronous training difficult problems of GANs and VAEs, recently, researchers propose Generative Scattering Networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain the features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate the image.

Image Generation

SLNSpeech: solving extended speech separation problem by the help of sign language

no code implementations21 Jul 2020 Jiasong Wu, Taotao Li, Youyong Kong, Guanyu Yang, Lotfi Senhadji, Huazhong Shu

After that, an improved U-Net with skip connections in feature extraction stage is applied for learning the embeddings among the mixed spectrogram transformed from source audios, the sign language features and visual features.

Self-Supervised Learning Speech Separation

Deep Octonion Networks

no code implementations20 Mar 2019 Jiasong Wu, Ling Xu, Youyong Kong, Lotfi Senhadji, Huazhong Shu

In recent years, the deep complex networks (DCNs) and the deep quaternion networks (DQNs) have attracted more and more attentions.

General Classification Image Classification +1

Compressing complex convolutional neural network based on an improved deep compression algorithm

no code implementations6 Mar 2019 Jiasong Wu, Hongshan Ren, Youyong Kong, Chunfeng Yang, Lotfi Senhadji, Huazhong Shu

Although convolutional neural network (CNN) has made great progress, large redundant parameters restrict its deployment on embedded devices, especially mobile devices.

Modulated binary cliquenet

no code implementations27 Feb 2019 Jinpeng Xia, Jiasong Wu, Youyong Kong, Pinzheng Zhang, Lotfi Senhadji, Huazhong Shu

Although Convolutional Neural Networks (CNNs) achieve effectiveness in various computer vision tasks, the significant requirement of storage of such networks hinders the deployment on computationally limited devices.

Fractional spectral graph wavelets and their applications

no code implementations27 Feb 2019 Jiasong Wu, Fuzhi Wu, Qihan Yang, Youyong Kong, Xilin Liu, Yan Zhang, Lotfi Senhadji, Huazhong Shu

One of the key challenges in the area of signal processing on graphs is to design transforms and dictionaries methods to identify and exploit structure in signals on weighted graphs.

Fractional Wavelet Scattering Network and Applications

no code implementations30 Jun 2018 Li Liu, Jiasong Wu, Dengwang Li, Lotfi Senhadji, Huazhong Shu

Results: The error rates for different fractional orders of FrScatNet are examined and show that the classification accuracy is significantly improved in fractional scattering domain.

General Classification Image Classification

Demystifying AlphaGo Zero as AlphaGo GAN

no code implementations24 Nov 2017 Xiao Dong, Jiasong Wu, Ling Zhou

The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear.

How deep learning works --The geometry of deep learning

no code implementations30 Oct 2017 Xiao Dong, Jiasong Wu, Ling Zhou

Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective.

Template Matching

MomentsNet: a simple learning-free method for binary image recognition

no code implementations22 Feb 2017 Jiasong Wu, Shijie Qiu, Youyong Kong, Yang Chen, Lotfi Senhadji, Huazhong Shu

In this paper, we propose a new simple and learning-free deep learning network named MomentsNet, whose convolution layer, nonlinear processing layer and pooling layer are constructed by Moments kernels, binary hashing and block-wise histogram, respectively.

PCANet: An energy perspective

no code implementations3 Mar 2016 Jiasong Wu, Shijie Qiu, Youyong Kong, Longyu Jiang, Lotfi Senhadji, Huazhong Shu

The principal component analysis network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.

General Classification

Kernel principal component analysis network for image classification

no code implementations20 Dec 2015 Dan Wu, Jiasong Wu, Rui Zeng, Longyu Jiang, Lotfi Senhadji, Huazhong Shu

In order to classify the nonlinear feature with linear classifier and improve the classification accuracy, a deep learning network named kernel principal component analysis network (KPCANet) is proposed.

Classification Face Recognition +3

Color Image Classification via Quaternion Principal Component Analysis Network

no code implementations5 Mar 2015 Rui Zeng, Jiasong Wu, Zhuhong Shao, Yang Chen, Lotfi Senhadji, Huazhong Shu

The Principal Component Analysis Network (PCANet), which is one of the recently proposed deep learning architectures, achieves the state-of-the-art classification accuracy in various databases.

Classification General Classification +1

Multilinear Principal Component Analysis Network for Tensor Object Classification

no code implementations5 Nov 2014 Rui Zeng, Jiasong Wu, Zhuhong Shao, Lotfi Senhadji, Huazhong Shu

The recently proposed principal component analysis network (PCANet) has been proved high performance for visual content classification.

Classification General Classification +1

Tensor object classification via multilinear discriminant analysis network

no code implementations5 Nov 2014 Rui Zeng, Jiasong Wu, Lotfi Senhadji, Huazhong Shu

The MLDANet is a variation of linear discriminant analysis network (LDANet) and principal component analysis network (PCANet), both of which are the recently proposed deep learning algorithms.

Classification General Classification +1

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