Neural Network Layer Compression for Image Analysis
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Solution Overview
Problem
Complex neural networks used for image analysis are computationally intensive and memory-hungry due to their large number of parameters and layers, leading to time-consuming operations and high storage requirements.
Innovation Solution
A method and system for compressing a teacher neural network by identifying and utilizing only the subset of artificial neural layers necessary for a specific task, forming a student network, which reduces the number of layers and parameters, thereby decreasing processing time and storage needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a complex neural network with many layers and parameters is used to classify images in over 1000 categories, then the classification capability and accuracy are improved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent segments the complex neural network into multiple functional modules or layers, allowing selective execution of only those segments necessary for the current classification task. This divides the monolithic network into manageable pieces that can be executed efficiently based on task requirements.
Solution Approach 2:
The patent applies partial action by executing only a subset of the neural network layers or parameters that are relevant to the specific classification task at hand, rather than running the entire network. This reduces computational overhead while maintaining classification accuracy for the given task.
2Adaptability or versatility
If a complex neural network with many parameters is used to classify images in over 1000 categories, then the classification capability is improved, but the memory requirements increase significantly
Solution Approach 1:
The patent extracts and removes unnecessary parameters, layers, or components from the complex neural network that are not required for the specific classification task. This extraction process reduces the memory footprint while preserving the essential classification functionality.
Solution Approach 2:
The patent segments the neural network parameters into task-relevant and task-irrelevant groups, storing and processing only the relevant parameters in memory during inference, thereby reducing overall memory requirements.
3Measurement precision
If a complex neural network with many layers is used for image analysis, then the object identification accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent introduces dynamic adaptability into the neural network, allowing the network structure or activation of layers to change dynamically based on the input data characteristics or task requirements. This enables the network to simplify its structure when full complexity is not needed.
Solution Approach 2:
The patent segments the complex network into modular components that can be independently activated or deactivated, reducing the effective complexity for specific tasks while maintaining high accuracy when needed.
Data Source
AI summary
Systems and methods are provided relating to artificial neural networks are provided. The systems and methods obtain a teacher network that includes artificial neural layers configured to automatically identify one or more objects in an image examined by the artificial neural layers, receive a set of task images at the teacher network, examine the set of task images with the teacher network, identify a subset of the artificial neural layers that are utilized during examination of the set of task images with the teacher network, and define a student network based on the set of task images. The student network is configured to automatically identify one or more objects in an image examined by the subset.