Neural Architecture Search Model Selection via Receptive Field Scoring
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Solution Overview
Problem
Current neural architecture search (NAS) methods are computationally intensive, time-consuming, and memory-dependent, often requiring extensive training and validation to select an optimal neural network model, and existing zero-cost proxies assign equal weightage to all computation nodes, leading to inappropriate model selection.
Innovation Solution
The method involves measuring the receptive field scale for each neural network layer, determining weighted scores based on these scales, and selecting the candidate AI model with the highest combined score for training and deployment, allowing for more accurate model selection without extensive training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NAS methods train multiple neural network models to select the optimal architecture, then model selection accuracy is improved, but computational time and resource consumption increase significantly
Solution Approach 1:
The patent applies preliminary action by performing receptive field analysis and scoring on neural network architectures before actual training. The system calculates receptive field scales and assigns scores to candidate models based on their architectural properties, enabling early elimination of suboptimal models without requiring full training cycles. This preliminary assessment significantly reduces computational time while maintaining selection accuracy.
2Productivity
If zero-cost proxy methods are used to estimate model performance without training, then computational load is reduced, but model selection accuracy deteriorates due to equal weightage assignment to all computation nodes
Solution Approach 1:
The patent applies local quality by assigning different weightages to different computation nodes based on their receptive field scales. Instead of treating all nodes equally, the system identifies that nodes with larger receptive fields (typically in deeper layers) contribute more significantly to model performance. By locally adapting the scoring mechanism to account for receptive field variations, the method achieves both computational efficiency and accurate model selection.
3Measurement precision
If extensive training is performed to validate neural network architectures, then model performance prediction accuracy is improved, but memory consumption and power usage increase
Solution Approach 1:
The patent applies the taking out principle by extracting and analyzing only the critical architectural property - receptive field scale - without requiring full model training. The system extracts receptive field information from the network architecture itself and uses this extracted feature for model comparison and selection. This extraction approach avoids the memory-intensive process of training multiple models while still achieving accurate performance prediction.
4Reliability
If multiple neural network models are trained and tested to find the optimal architecture, then model selection reliability is improved, but device complexity and operational overhead increase
Solution Approach 1:
The patent applies parameter changes by shifting the basis of model evaluation from performance metrics obtained through extensive training to architectural parameters - specifically receptive field scales - that can be calculated directly from the network structure. By changing the evaluation parameter from post-training accuracy to pre-training architectural property, the system maintains reliable model selection while significantly reducing operational complexity and resource requirements.
Data Source
AI summary
A method for selecting an artificial intelligence (AI) model in neural architecture search, includes: measuring a scale of receptive field for a plurality of neural network layers corresponding to each of a plurality of candidate AI models; determining a first score for a first group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the first group of neural network layers, the scale of the receptive field for each of the first group of neural network layers being smaller than a size of an object; determining a second score for a second group of neural network layers among the plurality of neural network layers based on the scale of the receptive field for the second group of neural network layers, the scale of the receptive field for each of the second group of neural network layers being greater than the size of the object; determining a third score for each of the plurality of candidate AI models as a function of the first score and the second score; and selecting, based on the third score, a candidate AI model among the plurality of candidate AI models for training and deployment, the candidate AI model having a highest third score among the third scores of the plurality of candidate AI models.