Hierarchical Invariant Representation for Neural Network Image Recognition
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Solution Overview
Problem
Current digital image recognition methods using artificial neural networks fail to adequately capture the hierarchical structure of signals, leading to inadequate invariance under transformations such as rotations, requiring programmers to manually design networks or use resource-intensive data augmentation.
Innovation Solution
A method involving sparse coding dictionary learning, computation of correlation matrices, and eigenvectors of the Laplacian operator to infer transformations and create invariant representations, allowing the neural network to recognize entities regardless of their position or orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard artificial neural networks are used for digital image recognition, then the network can process images and identify objects, but it fails to capture the hierarchical structure of signals and achieves inadequate invariance under transformations
Solution Approach 1:
The patent segments the signal processing into multiple hierarchical levels, where each level processes specific features. The hierarchical signal processing architecture divides the input signal into different levels of abstraction, with each level capturing specific structural characteristics. This segmentation allows the system to progressively build invariant representations from basic features to complex hierarchical structures, resolving the contradiction between maintaining reliability under transformations and managing device complexity.
Solution Approach 2:
The patent implements nested hierarchical levels where lower-level features are embedded within higher-level structures. Each hierarchical level contains and processes representations from the previous level, creating a nested structure similar to nested dolls. This nesting enables the system to maintain invariant representations at each level while building increasingly complex hierarchical understanding, thereby improving transformation invariance without proportionally increasing overall system complexity.
2Reliability
If programmers manually design network structures to achieve transformation invariance, then invariance under transformations improves, but the ease of manufacture and adaptability decreases
Solution Approach 1:
The patent enables the neural network to automatically learn and discover transformation invariances through hierarchical signal processing, rather than requiring manual programming. The system self-organizes its hierarchical structure and transformation representations by processing training data through multiple levels, automatically adapting to the specific transformation types present in the data. This self-service approach eliminates the need for manual network design while maintaining high transformation invariance.
Solution Approach 2:
The patent implements dynamic hierarchical structures that can adapt and reconfigure based on the input data and transformation types. Rather than fixed manual designs, the hierarchical levels and their connections are dynamically adjusted during training to optimize transformation invariance. This dynamic approach allows the system to automatically adapt to different transformation scenarios without requiring reprogramming, thereby improving ease of manufacture while maintaining reliability.
3Reliability
If data augmentation is used to increase transformation variability in training, then invariance under transformations improves, but resource consumption and training time increase significantly
Solution Approach 1:
The patent performs preliminary hierarchical feature extraction and transformation analysis during the network design phase, before actual training begins. By pre-identifying the relevant hierarchical levels and transformation types through analysis of the data structure, the system prepares an optimized hierarchical architecture that naturally handles transformations. This preliminary action eliminates the need for extensive data augmentation during training, as the hierarchical structure is already configured to be invariant to the identified transformations, thereby improving training efficiency while maintaining reliability.
Solution Approach 2:
The patent extracts and separates the transformation invariant features at each hierarchical level, isolating the essential structural characteristics from the transformation variations. By extracting only the invariant hierarchical features and discarding transformation-specific variations, the system achieves transformation invariance without needing to train on numerous transformed versions of each sample. This extraction approach significantly reduces the effective training data requirements and improves productivity while maintaining high invariance reliability.
Data Source
AI summary
A method for processing digital image recognition of invariant representations of hierarchically structured entities can be performed by a computer using an artificial neural network. The method involves learning a sparse coding dictionary on an input signal to obtain a representation of low-complexity components. Possible transformations are inferred from the statistics of the sparse representation by computing a correlation matrix. Eigenvectors of the Laplacian operator on the graph whose adjacency matrix is the correlation matrix from the previous step are computed. A coordinate transformation is performed to the base of eigenvectors of the Laplacian operator, and the first step is repeated with the next higher hierarchy level until all hierarchy levels of the invariant representations of the hierarchically structured entities are processed and the neural network is trained. The trained artificial neural network can then be used for digital image recognition of hierarchically structured entities.