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公开(公告)号：US10970617B2
公开(公告)日：2021-04-06
申请号：US15753520
申请日：2015-08-21
发明人： Jian Cheng , Jiaxiang Wu , Cong Leng , Hanqing Lu
摘要： An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter provided by the present application comprises: quantizing the parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of subcode books; acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and respective corresponding index values of the plurality of subcode books. The present application may implement the acceleration and compression for a deep convolutional neural network.
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公开(公告)号：US09996764B2
公开(公告)日：2018-06-12
申请号：US15306983
申请日：2014-04-29
发明人： Jian Cheng , Cong Leng , Jiaxiang Wu , Hanqing Lu
CPC分类号： G06K9/4633 , G06F17/30256 , G06F17/30268 , G06F17/30271 , G06K9/4671 , G06K9/6215
摘要： An image matching method based on cascaded binary encoding includes using a hashing look-up with multiple hashing tables to coarsely filter candidate key-points in an image to produce a candidate subset of key-points, projecting the candidate subset into a high-dimensional Hamming space, and building a “Hamming distance-memory address” hashing table. An optimal matching key-point is discovered by querying the hashing table. The image matching method has high processing speed and matching quality, which can be used for efficient and accurate image matching.
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3.发明申请公开(公告)号：US20180247180A1
公开(公告)日：2018-08-30
申请号：US15753520
申请日：2015-08-21
发明人： Jian Cheng , Jiaxiang Wu , Cong Leng , Hanqing Lu
CPC分类号： G06N3/04 , G06F17/16 , G06K9/4628 , G06K9/6243 , G06K9/6274 , G06N3/0454 , G06N3/08 , G06N20/00
摘要： An acceleration and compression method for a deep convolutional neural network based on quantization of a parameter provided by the present application comprises: quantizing the parameter of the deep convolutional neural network to obtain a plurality of subcode books and respective corresponding index values of the plurality of subcode books; acquiring an output feature map of the deep convolutional neural network according to the plurality of subcode books and respective corresponding index values of the plurality of subcode books. The present application may implement the acceleration and compression for a deep convolutional neural network.
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