Shared CNN Encoder Architecture for Multi-Task Scale Matching
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Solution Overview
Problem
Determining an encoder architecture for a shared convolutional neural network that can efficiently process multiple image processing tasks is a non-trivial task, requiring reduced computational effort and resource efficiency.
Innovation Solution
A method for determining the architecture of a shared encoder in a convolutional neural network involves calculating characteristic scale distributions, generating encoder architecture candidates, assessing receptive field sizes, and selecting an encoder architecture based on assessment measures to ensure optimal matching with task-specific decoders, thereby reducing computational effort and resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If a shared encoder is used to process multiple image processing tasks, then resource efficiency and computational effort are improved, but determining the optimal encoder architecture becomes complex and non-trivial
Solution Approach 1:
The patent changes the approach from trial-and-error architecture selection to a systematic method based on calculating characteristic scale distributions and matching them with encoder layer receptive fields. By parameterizing the architecture selection process around scale distribution matching, the system resolves the contradiction between resource efficiency and architectural complexity determination.
Solution Approach 2:
The patent performs preliminary calculations of characteristic scale distributions for each task and receptive field sizes for encoder layers before finalizing the architecture. This preliminary analysis enables informed architecture selection without requiring extensive trial-and-error training, thus reducing both computational effort and complexity in the design phase.
2Productivity
If the encoder architecture is optimized for specific tasks, then processing efficiency is improved, but the complexity of determining the architecture increases
Solution Approach 1:
The patent creates a universal methodology for determining encoder architecture that works across multiple different image processing tasks. The characteristic scale distribution approach provides a task-agnostic framework that can be applied universally, simplifying the determination process while maintaining task-specific optimization through the matching process.
Solution Approach 2:
The patent transforms the architecture determination problem into a parameter-matching problem between characteristic scale distributions and receptive field sizes. This parameter-based approach enables systematic optimization for specific tasks while maintaining a consistent determination methodology, thus improving processing efficiency without proportionally increasing determination complexity.
3Measurement precision
If multiple encoder architecture candidates are evaluated, then the quality of matching to task requirements is improved, but the computational effort increases
Solution Approach 1:
The patent performs preliminary evaluation of multiple architecture candidates using characteristic scale distribution matching before committing to extensive training. This preliminary assessment based on receptive field size calculations allows the system to identify promising architectures without requiring full training cycles, thus improving matching quality while controlling computational effort.
Solution Approach 2:
The patent changes the evaluation metric from performance-based (requiring full training) to parameter-based (receptive field matching). This parameter change enables quick assessment of multiple architecture candidates through simple calculations, improving matching quality assessment while significantly reducing the computational effort required compared to training-based evaluation.
Data Source
AI summary
A method for determining an encoder architecture of a convolutional neural network configured to process image processing tasks. For each image processing task), characteristic scale distribution is calculated based on training data. Encoder architecture candidates are generated, each including a shared encoder layer providing computational operations for image processing tasks and branches which span over encoder layers providing at least partly different computational operations for the image processing tasks. Each branch is associated with a certain image processing task. Receptive encoder layer field sizes and assessment measures are calculated, each assessment measure referring to a combination of a certain encoder architecture and a certain image processing task, and including information regarding matching quality of characteristic scale distribution associated with the assessment measure to the receptive field sizes of the encoder layers. The assessment measures are compared and a comparison result established. An encoder architecture is selected based on the comparison result.