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公开(公告)号：US20220391672A1
公开(公告)日：2022-12-08
申请号：US17820972
申请日：2022-08-19
Inventor： Kafeng Wang , Haoyi Xiong , Chengzhong Xu , Dejing Dou
Abstract: The disclosure provides a multi-task deployment method, and an electronic device. The method includes: obtaining N first tasks and K network models, in which N and K are positive integers greater than or equal to 1; allocating the N first tasks to the K network models differently for operation, to obtain at least one candidate combination of tasks and network models, in which each candidate combination includes a mapping relation between the N first tasks and the K network models; selecting a target combination with a maximum combination operation accuracy from the at least one candidate combination; and deploying a target mapping relation comprised in the target combination and the K network models on a prediction machine.
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2.发明公开公开(公告)号：US20240104906A1
公开(公告)日：2024-03-28
申请号：US18099551
申请日：2023-01-20
Inventor： Xuhong Li , Jiamin Chen , Haoyi Xiong , Dejing Dou
IPC: G06V10/776
CPC classification number: G06V10/776
Abstract: Provided is a model interpretation method, an image processing method, an electronic device and a storage medium, relating to the field of artificial intelligence, in particular to the field of deep learning. The model interpretation method includes: obtaining a token vector corresponding to an image feature input to a first model; obtaining a model prediction result output by the first model; and determining, according to a combination of an attention weight and a gradient, an association relation between the token vector input to the first model and the model prediction result output by the first model, where the association relation is used to characterize interpretability of the first model.
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公开(公告)号：US11928563B2
公开(公告)日：2024-03-12
申请号：US17355347
申请日：2021-06-23
Inventor： Xingjian Li , Haoyi Xiong , Dejing Dou
IPC: G06N20/00 , G06F18/214 , G06F18/22 , G06F18/24 , G06F18/25 , G06N5/04 , G06V10/774 , G06V10/82 , G06V10/84
CPC classification number: G06N20/00 , G06F18/214 , G06F18/22 , G06F18/24 , G06F18/251 , G06N5/04 , G06V10/774 , G06V10/82 , G06V10/84
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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公开(公告)号：US20220191270A1
公开(公告)日：2022-06-16
申请号：US17687123
申请日：2022-03-04
Inventor： Ji Liu , Xiyue Zhang , Haoyi Xiong , Dejing Dou , Shilei Ji
Abstract: A method of data interaction, a data interaction apparatus, an electronic device and a non-transitory computer readable storage medium are provided, related to field of computer technologies, and in particular to the field of artificial intelligence technologies. When the method of data interaction is applied to the first data platform, the method includes: sending a data request to a second data platform based on a first resource server provided by a cloud or based on a local server; acquiring response data fed back by the second data platform based on a second resource server provided by the cloud; at least one of the first resource server and the second resource server is dynamically created by the cloud.
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