Neural Network Architecture Search via Mutual Information Maximization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image classification methods based on neural networks require complex and resource-intensive manual design, leading to high computational costs and lengthy training times, limiting their practicality in industrial applications.
Innovation Solution
An image classification method that automatically determines the neural network architecture and parameters by maximizing mutual information between the training image and the neural network, using a super-network and architecture-generating network to iteratively update parameters until convergence, thereby reducing design time and human resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual feature extraction operators are used, then the image processing can be performed with simple acquisition devices, but the classification performance cannot exceed human experts and requires significant human resource consumption
Solution Approach 1:
The system enables machines to automatically design and optimize neural network architectures for image classification tasks without requiring manual intervention from experts. The automated architecture search algorithm independently performs feature extraction, classification, and optimization operations, making the system self-sufficient and eliminating the need for human experts to manually design feature operators.
Solution Approach 2:
The patent replaces manual mechanical design processes with automated computational algorithms. Instead of human experts manually creating feature extraction operators, the system uses automated neural network architecture search that computationally explores and selects optimal architectures, substituting human intellectual labor with machine-based automated optimization.
2Measurement precision
If manually designed neural networks are used, then good performance can be achieved in image processing tasks, but the design process is complex and requires large human and computational resource consumption
Solution Approach 1:
The system automatically designs and optimizes neural network architectures without requiring manual intervention. The automated architecture search algorithm independently performs the complex design process, exploring numerous architectural configurations and selecting optimal ones based on performance metrics, thereby eliminating the complexity burden from manual design.
Solution Approach 2:
The patent employs automated optimization algorithms that systematically vary and optimize network parameters such as architecture topology, layer configurations, and hyperparameters. This automated parameter search and optimization process replaces complex manual design decisions with computational exploration, achieving high accuracy while simplifying the design process.
3Loss of time
If existing automatically designed neural networks are used, then design time is reduced, but computational cost is high and training time is lengthy
Solution Approach 1:
The patent optimizes computational efficiency by dynamically adjusting search space parameters and training parameters based on problem complexity. The automated architecture search algorithm adapts its exploration strategy to balance computational cost with design time reduction, using parameter optimization to achieve efficient training without excessive computational expenditure.
Solution Approach 2:
The system employs partial architecture search strategies that focus computational resources on the most promising architectural directions rather than exhaustively searching all possibilities. This selective exploration approach reduces overall computational cost while still achieving high-quality architecture designs, avoiding the need to compute every possible configuration.
4Adaptability or versatility
If existing image classification methods are used, then they can process natural images, but they are less practical for industrial applications due to high resource consumption and specific data set orientation
Solution Approach 1:
The patent designs a universal automated architecture search framework that can adapt to different image classification tasks and data types. The system learns generalizable architectural patterns from training data and automatically configures networks suited for specific applications, whether natural images, medical images, or other industrial image data, thereby achieving broad adaptability without requiring task-specific manual redesign.
Solution Approach 2:
The system automatically adapts to different industrial applications by independently analyzing task requirements and data characteristics. The automated architecture search algorithm self-configures network parameters and architectures tailored to each specific application scenario, eliminating the need for manual reconfiguration and enabling rapid deployment across diverse industrial image classification tasks.
Data Source
AI summary
The present disclosure provides an image classification method for maximizing mutual information, device, medium and system, the method including: acquiring a training image; maximizing the mutual information between the training image and a neural network architecture, and automatically determining the network architecture and parameter of the neural network; and processing image data to be classified using the obtained neural network to obtain an image classification result. According to the present disclosure, the network architecture and parameter of the neutral network are automatically designed and determined by maximizing the mutual information based on given image data without burdensome manual design and saving human and computational resource consumption. The present disclosure can automatically design and obtain a neural network-based image classification method in a very short time, and at the same time can achieve higher image classification accuracy.