Intermediate Feature Map Adaptation for AI Detection Networks
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Solution Overview
Problem
Existing AI model relearning methods require excessive learning times and data transmission, leading to prolonged adaptation times and increased communication data, and pose risks of personal information leakage.
Innovation Solution
Adapt the AI model by generating or selecting subsequent networks based on intermediate feature maps, reducing the number of learning iterations and data transmission, while minimizing personal information exposure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the entire AI model is relearned for adaptation, then the AI model can be adapted to use environmental conditions, but the number of learning times increases and adaptation time becomes excessive
Solution Approach 1:
The AI model is divided into a preceding network and a subsequent network. Only the subsequent network is relearned for adaptation to new environmental conditions, while the preceding network remains unchanged. This segmentation allows selective relearning of only the necessary portion of the model, significantly reducing adaptation time while maintaining adaptability to new conditions.
2Adaptability or versatility
If the entire AI model is relearned for adaptation, then the AI model can be adapted to use environmental conditions, but the number of input data pieces increases and communication data amount increases
Solution Approach 1:
The AI model is divided into a preceding network and a subsequent network. Only the subsequent network requires input data for relearning, significantly reducing the quantity of data that needs to be transmitted and stored compared to relearning the entire model. This segmentation reduces communication data requirements while maintaining adaptation capability.
3Adaptability or versatility
If actually captured images are transmitted to the cloud side for relearning, then the AI model can be adapted to use environmental conditions, but personal information leakage risk increases
Solution Approach 1:
Only the necessary intermediate feature map is extracted and transmitted from the edge device to the cloud, rather than transmitting complete captured images. This extraction approach obtains only the essential data needed for relearning the subsequent network, significantly reducing personal information leakage risk while maintaining adaptation capability.
4Object-affected harmful factors
If intermediate feature map is used instead of captured images, then personal information leakage is reduced, but the feature map processing complexity increases
Solution Approach 1:
The processing complexity is segmented and distributed: the edge device performs the computationally intensive task of generating the intermediate feature map using the preceding network, while the cloud device performs the simpler task of relearning the subsequent network using this feature map. This segmentation reduces overall system complexity while maintaining personal information protection.
Data Source
AI summary
An information processing device according to the present technology includes: a subsequent network acquisition processing unit that receives, from an external device, an intermediate feature map obtained in a predetermined intermediate layer of an artificial intelligence model when input data is given to the artificial intelligence model, which has a neural network and receives detection data from a sensor device as the input data, and selects one of subsequent networks from among a plurality of candidate subsequent networks based on the intermediate feature map, the subsequent networks being networks subsequent to the predetermined intermediate layer in the artificial intelligence model, or generates one of the subsequent networks based on a trained network serving as a base and the intermediate feature map; and a transmission processing unit that performs processing of transmitting configuration data of the subsequent network selected or generated by the subsequent network acquisition unit to the outside.