Autonomous Transfer Model Selection via Data Similarity
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Solution Overview
Problem
Current machine learning tasks face challenges in selecting the most appropriate pre-trained neural networks for transfer learning due to limited data availability and the time-consuming nature of training models, which affects the performance of target machine learning tasks.
Innovation Solution
A system and method that autonomously assesses similarity metrics between source and target data sets to identify and select pre-trained neural network models, allowing for efficient and effective selection of models from a library or generation of new models by combining layers from existing models based on similarity metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pre-trained neural networks are selected for transfer learning, then the performance of target machine learning tasks is enhanced, but the selection process becomes complex and time-consuming
Solution Approach 1:
The system automatically assesses similarity metrics between source and target data sets and autonomously identifies suitable pre-trained neural networks without requiring manual intervention. The assessment component and identification component work together to self-service the model selection process, reducing complexity while maintaining performance enhancement.
Solution Approach 2:
The system changes the approach from manual model selection to automated selection based on similarity metric parameters. By transforming the selection criterion into a measurable parameter (similarity metric between data sets), the complex selection process becomes systematic and automated, resolving the contradiction between performance enhancement and process complexity.
2Reliability
If extensive training is performed to improve model accuracy, then the performance of machine learning tasks improves, but the training time increases significantly
Solution Approach 1:
The system performs preliminary action by selecting pre-trained neural networks that are already trained on source data sets with high similarity to target data sets. This preliminary training is done beforehand, so when applying to target tasks, the model can be fine-tuned quickly without requiring extensive training from scratch, thus reducing training time while maintaining accuracy.
Solution Approach 2:
The system copies the pre-trained neural network models that have been trained on similar data sets and adapts them to target tasks through transfer learning. Instead of creating new models from scratch requiring extensive training, the system copies and adapts existing trained models, significantly reducing the training time needed while achieving high accuracy.
3Adaptability or versatility
If new neural network models are generated by combining layers from existing models, then model versatility improves, but the process becomes more complex
Solution Approach 1:
The system segments the model generation process into two distinct phases: (1) assessment of similarity metrics between data sets, and (2) identification and selection of pre-trained models. This segmentation simplifies the complex task of generating versatile models by breaking it down into manageable steps, making the process less complex while maintaining versatility.
Solution Approach 2:
The system creates a universal approach that works for various target machine learning tasks by automatically assessing similarity metrics and identifying suitable pre-trained models. This universal method can be applied to different tasks without requiring task-specific complex generation processes, thus improving versatility while reducing process complexity through a unified automated approach.
Data Source
AI summary
Techniques regarding autonomously facilitating the selection of one or more transfer models to enhance the performance of one or more machine learning tasks are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise an assessment component that can assess a similarity metric between a source data set and a sample data set from a target machine learning task. The computer executable components can also comprise an identification component that can identify a pre-trained neural network model associated with the source data set based on the similarity metric to perform the target machine learning task.