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Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla

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发表于 2025-3-21 18:24:24 | 显示全部楼层 |阅读模式
书目名称Neural Information Processing
副标题26th International C
编辑Tom Gedeon,Kok Wai Wong,Minho Lee
视频video
丛书名称Communications in Computer and Information Science
图书封面Titlebook: Neural Information Processing; 26th International C Tom Gedeon,Kok Wai Wong,Minho Lee Conference proceedings 2019 Springer Nature Switzerla
描述The two-volume set CCIS 1142 and 1143 constitutes thoroughly refereed contributions presented at the 26th International Conference on Neural Information Processing, ICONIP 2019, held in Sydney, Australia, in December 2019..For ICONIP 2019 a total of 345 papers was carefully reviewed and selected for publication out of 645 submissions. The 168 papers included in this volume set were organized in topical sections as follows: adversarial networks and learning; convolutional neural networks; deep neural networks; embeddings and feature fusion; human centred computing; human centred computing and medicine; human centred computing for emotion; hybrid models; image processing by neural techniques; learning from incomplete data; model compression and optimization; neural network applications; neural network models; semantic and graph based approaches; social network computing; spiking neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models..
出版日期Conference proceedings 2019
关键词artificial intelligence; computer networks; computer science; computer systems; computer vision; database
版次1
doihttps://doi.org/10.1007/978-3-030-36802-9
isbn_softcover978-3-030-36801-2
isbn_ebook978-3-030-36802-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2019
The information of publication is updating

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G-HAPNet: A Novel Structure for Single Image Super-Resolutionxtensive experiments demonstrate that with the same level depth and computational budgets, our proposed G-HAPNet has better performance than state-of-the-art methods on both synthetic datasets and real-world dataset, which indicates our G-HAPNet is a more efficient and practical structure for SISR.
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Delving into Precise Attention in Image Captioningieve comparable results with the state-of-the-art under cross entropy loss without any bells and whistles on MSCOCO dataset. Furthermore, our model can improve the performance under different encoders and decoders.
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Image Generation Framework for Unbalanced License Plate Data Setour method on the Chinese license plates, and the unbalance of the license plate data set is shown in the provincial category and the special license plates. The Inception Score and the FID Score are used as metrics to validate our method. The experimental results show that the Inception Score of th
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1865-0929 neuron and related models; text computing using neural techniques; time-series and related models; and unsupervised neural models..978-3-030-36801-2978-3-030-36802-9Series ISSN 1865-0929 Series E-ISSN 1865-0937
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