Capsule Neural Network Generation for Image Processing
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Solution Overview
Problem
Existing image processing platforms are inefficient and inaccurate due to the lack of consideration for image orientation and feature placement, leading to false positives and negatives, and are vulnerable to data leakages, as they rely on convolutional neural networks that process all features simultaneously.
Innovation Solution
The system generates and trains capsule neural networks, which consider image orientation and feature placement, by transferring learning from existing convolutional neural networks, retraining based on validation failures, and replacing convolutional networks with capsule networks when accuracy thresholds are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If convolutional neural networks process all features simultaneously, then processing completeness is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent segments the image processing task by dividing features into different groups based on their orientation and placement characteristics. Instead of processing all features simultaneously through a single convolutional neural network, the system creates multiple specialized capsule neural networks that process specific feature groups in parallel, thereby maintaining processing completeness while improving efficiency.
Solution Approach 2:
The patent transitions from traditional 2D convolutional processing to 3D capsule processing by adding an orientation dimension. This dimensional expansion allows the system to process not only the presence of features but also their spatial orientation and hierarchical relationships, achieving more comprehensive processing with improved efficiency through specialized capsule units.
2Reliability
If convolutional neural networks process all features simultaneously, then feature coverage is improved, but accuracy deteriorates
Solution Approach 1:
The patent applies local quality by creating different types of capsule neural networks specialized for processing different feature characteristics. Each capsule network is optimized for specific orientations and feature placements, allowing high-precision processing of local feature patterns while maintaining comprehensive feature coverage through the ensemble of specialized networks.
3Measurement precision
If capsule neural networks are trained from scratch, then model accuracy is improved, but training time deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training capsule neural networks using transfer learning from pre-trained convolutional neural networks. The pre-trained CNNs provide initial weight configurations and feature representations that serve as a foundation for the capsule networks, significantly reducing training time while maintaining or improving accuracy through subsequent fine-tuning on capsule-specific tasks.
Data Source
AI summary
Embodiments of the present invention provide a system for generating capsule neural networks for enhancing image processing platforms. The system is configured for generate capsule neural network based on instructions received form at least one user, transfer learning from an existing image processing platform to train the capsule neural network, receive input from one or more devices and provide the input to the existing image processing platform comprising a convolutional neural network, wherein the convolutional neural network processes the input, activate the capsule neural network to validate the processing of the convolutional neural network, and retrain the capsule neural network based on the validations associated with the convolutional neural network.