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公开(公告)号：US20230244932A1
公开(公告)日：2023-08-03
申请号：US18076501
申请日：2022-12-07
Inventor： Ji LIU , Qilong LI , Yu LI , Xingjian LI , Yifan SUN , Dejing DOU
Abstract: Provided are an image occlusion method, a model training method, a device, and a storage medium, which relate to the technical field of artificial intelligence, in particular, to the field of computer vision technologies and deep learning, and may be applied to image recognition, model training and other scenarios. The specific implementation solution is as follows: generating a candidate occlusion region according to an occlusion parameter; according to the candidate occlusion region, occluding an image to be processed to obtain a candidate occlusion image; determining a target occlusion region from the candidate occlusion region according to visual security and data availability of the candidate occlusion image; and according to the target occlusion region, occluding the image to be processed to obtain a target occlusion image. In this manner, the image to be processed is desensitized while the accuracy of target recognition is ensured.
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公开(公告)号：US20230206123A1
公开(公告)日：2023-06-29
申请号：US18080803
申请日：2022-12-14
Inventor： Ji LIU , Hong ZHANG , Juncheng JIA , Ruipu ZHOU , Dejing DOU
CPC classification number: G06N20/00 , G06F9/4881
Abstract: A technical solution relates to distributed machine learning, and relates to the field of artificial intelligence technologies, such as machine learning technologies, or the like. An implementation includes: acquiring, based on delay information, an optimal scheduling queue of a plurality of edge devices participating in training; and scheduling each edge device of the plurality of edge devices to train a machine learning model based on the optimal scheduling queue of the plurality of edge devices.
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Patent Agency Ranking
公开(公告)号：US20230206075A1
公开(公告)日：2023-06-29
申请号：US17991077
申请日：2022-11-21
Inventor： Ji LIU , Zhihua WU , Danlei FENG , Minxu ZHANG , Xinxuan WU , Xuefeng YAO , Beichen MA , Dejing DOU , Dianhai YU , Yanjun MA
Abstract: A method for distributing network layers in a neural network model includes: acquiring a to-be-processed neural network model and a computing device set; generating a target number of distribution schemes according to network layers in the to-be-processed neural network model and computing devices in the computing device set, the distribution schemes including corresponding relationships between the network layers and the computing devices; according to device types of the computing devices, combining the network layers corresponding to the same device type in each distribution scheme into one stage, to obtain a combination result of each distribution scheme; obtaining an adaptive value of each distribution scheme according to the combination result of each distribution scheme; and determining a target distribution scheme from the distribution schemes according to respective adaptive value, and taking the target distribution scheme as a distribution result of the network layers in the to-be-processed neural network model.
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34.发明申请公开(公告)号：US20230013055A1
公开(公告)日：2023-01-19
申请号：US17945979
申请日：2022-09-15
Inventor： Yanyan LI , Jingbo ZHOU , Jizhou HUANG , Airong JIANG , Dejing DOU
Abstract: A method is provided. The method includes: determining, by one or more computers, a name of a target region, wherein the name of the target region is determined based on geometry attribute information of the target region; and determining, by one or more computers, region attribute information of the target region based on the name of the target region
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公开(公告)号：US20220385583A1
公开(公告)日：2022-12-01
申请号：US17817594
申请日：2022-08-04
Inventor： Ji LIU , Jiayuan ZHANG , Ruipu ZHOU , Dejing DOU
IPC: H04L47/22 , H04L47/2475
Abstract: A traffic classification method and apparatus, a training method and apparatus, a device and a medium are provided. An implementation is: performing a preprocessing operation on each characteristic of one or more characteristics of an object to be classified; and inputting the one or more characteristics of the object to be classified into a traffic classifier to determine a traffic type of the object to be classified. The preprocessing operation includes at least one of: setting, in response to determining that a characteristic value of the characteristic is invalid data, the characteristic value to a null value; converting, in response to determining that the characteristic is a non-numeric characteristic, the characteristic value of the characteristic to an integer value, and normalizing, in response to determining that the characteristic is a non-port characteristic, the characteristic value of the characteristic.
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36.发明申请公开(公告)号：US20220282992A1
公开(公告)日：2022-09-08
申请号：US17717872
申请日：2022-04-11
Inventor： Yanyan LI , Peng WANG , Hengshu ZHU , Shilin WU , Dejing DOU
Abstract: The present disclosure provides a method and apparatus for generating an electronic map, an electronic device and a storage medium, and relates to the field of data processing technology. A specific implementation comprises: establishing a plurality of groups of corresponding relationships between enterprise names and enterprise addresses using network data; determining respectively a fine-grained region where each enterprise address is located; and creating an enterprise electronic map of the fine-grained region based on the corresponding relationships between the enterprise names and the enterprise addresses and the fine-grained region where the each enterprise address is located. A corresponding relationship between an enterprise name and an enterprise address is established using existing network data.
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公开(公告)号：US20210319262A1
公开(公告)日：2021-10-14
申请号：US17355347
申请日：2021-06-23
Inventor： Xingjian LI , Haoyi XIONG , Dejing DOU
Abstract: The present application provides a model training, image processing method, device, storage medium, and program product relating to deep learning technology, which are able to screen auxiliary image data with image data for learning a target task, and further fuse the target image data and the auxiliary image data, so as to train a built and to-be-trained model with the fusion-processed fused image data. This implementation can increase the amount of data for training the model, and the data for training the model is determined is based on the target image data, which is suitable for learning the target task. Therefore, the solution provided by the present application can train an accurate target model even if the amount of target image data is not sufficient.
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