Convolutional neural network system
A convolutional neural network method includes: determining a temporary buffer layer between a first layer and a final layer of a convolutional neural network system; in a first stage, convolution operation is executed from a first layer to a temporary buffer layer of the convolutional neural networ...
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| creator | LIN YUNSHENG TANG DIWEN |
| description | A convolutional neural network method includes: determining a temporary buffer layer between a first layer and a final layer of a convolutional neural network system; in a first stage, convolution operation is executed from a first layer to a temporary buffer layer of the convolutional neural network system according to part of input data of a previous level of the temporary buffer layer so as togenerate a feature map line; and in the second stage, executing convolution operation from the temporary buffer layer to the final layer of the convolutional neural network system to generate a feature map.
一种卷积神经网络方法，其包含：决定一暂时缓冲层，其位于卷积神经网络系统的第一层与最终层之间；于第一阶段，从卷积神经网络系统的第一层至暂时缓冲层，根据该暂时缓冲层之前层级的部分输入数据，执行卷积操作以产生一特征图线；及于第二阶段，从卷积神经网络系统的暂时缓冲层至最终层，执行卷积操作以产生一特征图。 |
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一种卷积神经网络方法，其包含：决定一暂时缓冲层，其位于卷积神经网络系统的第一层与最终层之间；于第一阶段，从卷积神经网络系统的第一层至暂时缓冲层，根据该暂时缓冲层之前层级的部分输入数据，执行卷积操作以产生一特征图线；及于第二阶段，从卷积神经网络系统的暂时缓冲层至最终层，执行卷积操作以产生一特征图。</description><language>chi ; eng</language><subject>CALCULATING ; COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS ; COMPUTING ; COUNTING ; IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ; PHYSICS</subject><creationdate>2020</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201208&DB=EPODOC&CC=CN&NR=112052935A$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,776,881,25542,76289</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20201208&DB=EPODOC&CC=CN&NR=112052935A$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>LIN YUNSHENG</creatorcontrib><creatorcontrib>TANG DIWEN</creatorcontrib><title>Convolutional neural network system</title><description>A convolutional neural network method includes: determining a temporary buffer layer between a first layer and a final layer of a convolutional neural network system; in a first stage, convolution operation is executed from a first layer to a temporary buffer layer of the convolutional neural network system according to part of input data of a previous level of the temporary buffer layer so as togenerate a feature map line; and in the second stage, executing convolution operation from the temporary buffer layer to the final layer of the convolutional neural network system to generate a feature map.
一种卷积神经网络方法，其包含：决定一暂时缓冲层，其位于卷积神经网络系统的第一层与最终层之间；于第一阶段，从卷积神经网络系统的第一层至暂时缓冲层，根据该暂时缓冲层之前层级的部分输入数据，执行卷积操作以产生一特征图线；及于第二阶段，从卷积神经网络系统的暂时缓冲层至最终层，执行卷积操作以产生一特征图。</description><subject>CALCULATING</subject><subject>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</subject><subject>COMPUTING</subject><subject>COUNTING</subject><subject>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</subject><subject>PHYSICS</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2020</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZFB2zs8ry88pLcnMz0vMUchLLS0CUyXl-UXZCsWVxSWpuTwMrGmJOcWpvFCam0HRzTXE2UM3tSA_PrW4IDE5Fagh3tnP0NDIwNTI0tjU0ZgYNQAlQCcF</recordid><startdate>20201208</startdate><enddate>20201208</enddate><creator>LIN YUNSHENG</creator><creator>TANG DIWEN</creator><scope>EVB</scope></search><sort><creationdate>20201208</creationdate><title>Convolutional neural network system</title><author>LIN YUNSHENG ; TANG DIWEN</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_CN112052935A3</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>chi ; eng</language><creationdate>2020</creationdate><topic>CALCULATING</topic><topic>COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS</topic><topic>COMPUTING</topic><topic>COUNTING</topic><topic>IMAGE DATA PROCESSING OR GENERATION, IN GENERAL</topic><topic>PHYSICS</topic><toplevel>online_resources</toplevel><creatorcontrib>LIN YUNSHENG</creatorcontrib><creatorcontrib>TANG DIWEN</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>LIN YUNSHENG</au><au>TANG DIWEN</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>Convolutional neural network system</title><date>2020-12-08</date><risdate>2020</risdate><abstract>A convolutional neural network method includes: determining a temporary buffer layer between a first layer and a final layer of a convolutional neural network system; in a first stage, convolution operation is executed from a first layer to a temporary buffer layer of the convolutional neural network system according to part of input data of a previous level of the temporary buffer layer so as togenerate a feature map line; and in the second stage, executing convolution operation from the temporary buffer layer to the final layer of the convolutional neural network system to generate a feature map.
一种卷积神经网络方法，其包含：决定一暂时缓冲层，其位于卷积神经网络系统的第一层与最终层之间；于第一阶段，从卷积神经网络系统的第一层至暂时缓冲层，根据该暂时缓冲层之前层级的部分输入数据，执行卷积操作以产生一特征图线；及于第二阶段，从卷积神经网络系统的暂时缓冲层至最终层，执行卷积操作以产生一特征图。</abstract><oa>free_for_read</oa></addata></record> |
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| title | Convolutional neural network system |
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