Invention Application
- Patent Title: APPARATUS AND METHOD FOR TASK-ADAPTIVE NEURAL NETWORK RETRIEVAL BASED ON META-CONTRASTIVE LEARNING
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Application No.: US17731710Application Date: 2022-04-28
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Publication No.: US20220366240A1Publication Date: 2022-11-17
- Inventor: Sung Ju HWANG , Wonyong JEONG , Ha Yeon LEE , Geon PARK , Eun Young HYUNG
- Applicant: AITRICS CO., LTD. , KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
- Applicant Address: KR Seoul; KR Daejeon
- Assignee: AITRICS CO., LTD.,KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
- Current Assignee: AITRICS CO., LTD.,KOREA ADVANCED INSTITUTE OF SCIENCE AND TECHNOLOGY
- Current Assignee Address: KR Seoul; KR Daejeon
- Priority: KR10-2021-0055996 20210429,KR10-2022-0049434 20220421
- Main IPC: G06N3/08
- IPC: G06N3/08

Abstract:
Disclosed herein are an apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning. The apparatus for task-adaptive neural network retrieval based on meta-contrastive learning includes: memory configured to store a database including a learning model pool consisting of a plurality of datasets and neural networks pre-trained on the datasets and also store a program for task-adaptive neural network retrieval based on meta-contrastive learning; and a controller configured to perform task-adaptive neural network retrieval based on meta-contrastive learning by executing the program. In this case, the controller learns a cross-modal latent space for datasets and neural networks trained on the datasets by calculating the similarity between each dataset and a neural network trained on the dataset while considering constraints included in any one task previously selected from the database, thereby retrieving an optimal neural network.
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