-
公开(公告)号：US12067483B2
公开(公告)日：2024-08-20
申请号：US16431393
申请日：2019-06-04
发明人： Bin Wu , Fengwei Zhou , Zhenguo Li
摘要： Embodiments of the present invention provide a machine learning model training method, including: obtaining target task training data and N categories of support task training data; inputting the target task training data and the N categories of support task training data into a memory model to obtain target task training feature data and N categories of support task training feature data; training the target task model based on the target task training feature data and obtaining a first loss of the target task model, and separately training respectively corresponding support task models based on the N categories of support task training feature data and obtaining respective second losses of the N support task models; and updating the memory model, the target task model, and the N support task models based on the first loss and the respective second losses of the N support task models.
-
公开(公告)号：US20220375213A1
公开(公告)日：2022-11-24
申请号：US17880318
申请日：2022-08-03
发明人： Hang Xu , Zhili Liu , Fengwei Zhou , Jiawei Li , Xiaodan Liang , Zhenguo Li , Li Qian
IPC分类号： G06V10/82 , G06V10/77 , G06N3/063 , G06V10/28 , G06V10/774
摘要： A processing apparatus includes a collection module and a training module, the training module includes a backbone network and a region proposal network (RPN) layer, the backbone network is connected to the RPN layer, and the RPN layer includes a class activation map (CAM) unit. The collection module is configured to obtain an image, where the image includes an image with an instance-level label and an image with an image-level label. The backbone network is used to output a feature map of the image based on the image obtained by the collection module.
-
搜索结果
2 条记录 (耗时 0.013 秒)
