Task-Adaptive Neural Network Retrieval via Meta-Contrastive Learning
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Solution Overview
Problem
Current Neural Architecture Search (NAS) technologies can design optimal neural network architectures but struggle to generate parameters and require re-execution of processes when target datasets change, leading to high costs and inefficiencies.
Innovation Solution
An apparatus and method for task-adaptive neural network retrieval based on meta-contrastive learning that calculates similarity between datasets and pre-trained neural networks to learn a cross-modal latent space, enabling the retrieval of optimal neural networks with both architectures and parameters, thus reducing the need for re-execution and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If Neural Architecture Search (NAS) is used to design optimal neural network architectures, then the neural network architecture can be optimized, but parameters cannot be generated and all processes must be re-executed when target dataset changes
Solution Approach 1:
The patent pre-trains neural networks on multiple datasets and stores them in a database before actual retrieval is needed. This preliminary action allows the system to have ready-made models with both architectures and parameters, eliminating the need to re-execute entire NAS and parameter generation processes when new datasets arrive, thus resolving the contradiction between architecture optimization and parameter generation capability
Solution Approach 2:
The patent creates a database containing copies of pre-trained neural networks along with their architectures and parameters. When a new target dataset is provided, the system retrieves and copies relevant pre-trained models rather than regenerating them, enabling rapid adaptation without re-executing the complete training and search pipeline
2Manufacturing precision
If Neural Architecture Search (NAS) is used to design neural networks, then optimal architecture can be achieved, but high investment cost is required for research and development
Solution Approach 1:
The system performs expensive NAS and training operations in advance on a diverse set of datasets, storing the results in a database. When a new target dataset is provided, the system retrieves pre-computed models rather than re-executing the expensive NAS and training processes, thus reducing the research and development cost for each new task while maintaining optimal architecture quality
Solution Approach 2:
The patent stores copies of pre-trained neural networks, their architectures, and parameters in a database. This allows the system to retrieve and reuse optimized models for new datasets without incurring the full cost of re-running NAS and training, significantly reducing the ease of manufacture requirement while preserving architectural optimization benefits
3Adaptability or versatility
If all NAS and parameter generation processes are re-executed when target dataset changes, then the model can be adapted to new data, but time consumption increases
Solution Approach 1:
The system pre-trains and stores neural networks on multiple datasets in advance. When a new target dataset is provided, the system performs only retrieval operations from the pre-computed database rather than re-executing the time-consuming NAS and training processes, thus achieving rapid adaptation with minimal time loss
Solution Approach 2:
The patent retrieves copies of pre-trained neural networks from a database when new datasets are provided. This copying approach enables the system to adapt to new target datasets by simply loading and adjusting existing models rather than re-executing the complete training pipeline, significantly reducing the time required for adaptation while maintaining versatility
Data Source
AI summary
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.