Searchauto-encoderreturned 13 results

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1

Visual feature contrast decoupling for generalized zero-shot learning

视觉特征对比解耦的广义零样本学习

Zhang Zhiyuan

Yang Guan

Liu Xiaoming

Liu Yang

generalized zero-shot learning

decoupling representation learning

variational auto-encoder

generative model

feature fusion

No. 6, 2023 : 1912-1920 doi:10.19734/j.issn.1001-3695.2022.10.0534

2

Zero-shot learning based on stacked semantic auto-encoder with low-rank embedding

基于低秩堆栈式语义自编码器的零样本学习

Ran Ruisheng

Dong Shuhong

Li Jin

Wang Ning

image classification

zero-shot learning

stacked auto-encoder

low-rank embedding

No. 2, 2023 : 539-543 doi:10.19734/j.issn.1001-3695.2022.06.0302

3

Generative adversarial network and variational auto-encoder based outlier detection

基于生成对抗网络和变分自编码器的离群点检测算法

Jin Lina

Yu Jiong

Du Xusheng

Wang Song

data mining

outlier detection

generative adversarial network

variational auto-encoder

No. 3, 2022 : 774-779 doi:10.19734/j.issn.1001-3695.2021.07.0355

4

Feature learning method based on improved graph regularized auto-encoder

基于改进图正则项的自编码器特征学习算法

Wu Wenbin

Zhou Wei

Tang Dongming

feature representation

graph regularization

graph regularization

manifold learning

Kullback-Leibler divergence

robustness

unsupervised learning

No. 2, 2022 : 485-490 doi:10.19734/j.issn.1001-3695.2021.06.0223

5

Feature learning and action recognition method based on depth information

基于深度信息的特征学习与动作识别方法

Song Yihang

Hu Jing

Xu Chao

Meng Zhaopeng

interactive action recognition

depth information

hidden Markov model(HMM)

variational auto-encoder(VAE)

joint feature

No. 11, 2021 : 3446-3450 doi:10.19734/j.issn.1001-3695.2021.01.0067

6

Adaptive graph auto-encoder based on restarted random walk

基于重启随机游走的图自编码器

Li Lin

Liang Yongquan

Liu Guangming

graph embedding

network representation learning

graph auto-encoder

graph convolutional network

restarted random walk

adaptive learning strategy

No. 10, 2021 : 3009-3013 doi:10.19734/j.issn.1001-3695.2021.03.0083

7

Synthetic lethality prediction via supervised multi-view variational graph auto-encoder

基于有监督的多视角变分图自编码器的协同致死基因预测算法

Hao Zhifeng

Wu Di

Cai Ruichu

Chen Xuexin

Wen Wen

synthetic lethality

graph neural network

variational graph auto-encoder

multi-view

cancer

No. 9, 2021 : 2678-2682 doi:10.19734/j.issn.1001-3695.2021.01.0017

8

Abstractive text summarization based on variational auto-encoder

基于变分自编码器的生成式文本摘要研究

Huang Jiajia

Li Pengwei

abstractive text summarization

variational auto-encoder(VAE)

Seq2Seq model

coverage mechanism

pointer-generator network

No. 3, 2021 : 705-709 doi:10.19734/j.issn.1001-3695.2020.03.0051

9

Top-N recommendation algorithm based on multiple denoising auto-encoder

基于多重降噪自编码器模型的top-N推荐算法

Fang Yiqiu

Yu Chenxi

Ge Junwei

prediction accuracy

user rating

noise-adding operation

multiple denoising auto-encoder

No. 12, 2020 : 3582-3585 doi:10.19734/j.issn.1001-3695.2019.09.0534

10

Locally enhanced attribute network embedding via deep auto-encoder

基于深度自编码的局部增强属性网络表示学习

Chen Yiying

Zhang Shanshan

Chai Bianfang

network embedding

deep auto-encoder

attribute network

locally enhanced network embedding

No. 9, 2020 : 2610-2614 doi:10.19734/j.issn.1001-3695.2019.04.0126

11

Password cracking algorithm using conditional variational auto-encoders

基于条件变分自编码的密码攻击算法

Duan Dagao

Zhao Zhendong

Liang Shaohu

Han Zhongming

conditional variational auto-encoders

password guessing algorithm

passwords cracking

No. 3, 2020 : 821-823,837 doi:10.19734/j.issn.1001-3695.2018.08.0649

12

Recognition algorithm of emitter signal based on DAE+CNN

基于DAE+CNN辐射源信号识别算法

Ye Wenqiang

Yu Zhifu

Zhang Kui

radar emitter

short time Fourier transform(STFT)

denoising auto-encoder(DAE)

convolution neural network(CNN)

softmax

No. 12, 2019 : 3815-3818 doi:10.19734/j.issn.1001-3695.2018.07.0409

13

Semi-supervised auto-encoder using sparse and label regularizations for classification

稀疏和标签约束半监督自动编码器的分类算法

Wang Huiling

Song Wei

Wang Chenni

classification

sparse regularization

label regularization

auto-encoder

extreme learning machine

No. 9, 2019 : 2613-2617 doi:10.19734/j.issn.1001-3695.2018.02.0147