Deep Learning Network Selection for Pattern Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Pattern recognition systems face challenges in scalability and accuracy due to uneven distribution of data patterns, where less-popular patterns lead to inactive resources and overloading of popular pattern processors, and correlations between patterns are not considered effectively, resulting in inefficient resource allocation and duplication of pattern spaces.
Innovation Solution
A deep learning system with a reconfigurable grid architecture that selects the most suitable deep learning networks based on context and workload, using signature generation and deep-content classification to optimize query processing, and combines responses for efficient pattern recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If pattern recognition systems process all patterns uniformly, then each pattern can be recognized, but resources become inefficiently allocated with less-popular patterns causing inactive resources and popular patterns causing overloading
Solution Approach 1:
The system implements local quality by creating specialized pattern recognition resources tailored to specific pattern types. Instead of uniform processing, the system develops multiple specialized networks (e.g., CNNs for images, RNNs for sequences) that are optimally configured for their respective pattern domains, allowing each resource to operate at peak efficiency for its designated patterns.
Solution Approach 2:
The system dynamically changes parameters by selecting different deep learning networks based on pattern characteristics. The selection process adjusts system parameters (network architecture, processing mode) according to the input pattern type, enabling optimal resource allocation that adapts to the specific demands of each pattern rather than using fixed uniform processing.
2Speed
If the system stores copies of each known pattern in every machine, then pattern recognition is fast, but the system size and complexity increase significantly
Solution Approach 1:
The system applies segmentation by dividing the pattern recognition task into specialized segments handled by different deep learning networks. Instead of duplicating complete pattern databases across all machines, the system segments processing responsibilities among specialized networks, each handling specific pattern types, thereby reducing overall system complexity while maintaining recognition speed.
Solution Approach 2:
The system introduces an intermediary layer (the deep learning network selection mechanism) that routes patterns to appropriate specialized networks. This intermediary eliminates the need for direct pattern storage in every machine, as the selection mechanism directs queries to the appropriate specialized network, reducing system size while preserving fast recognition through optimized routing.
3Productivity
If the system considers correlations between patterns, then resource allocation improves, but the complexity of analyzing pattern relationships increases
Solution Approach 1:
The system performs preliminary action by pre-training multiple specialized deep learning networks on different pattern types and their correlations before actual operation. During runtime, the selection mechanism leverages these pre-established correlations without needing to analyze them in real-time, reducing operational complexity while maintaining efficient resource allocation based on learned pattern relationships.
Data Source
AI summary
A method for responding to a query is implemented on at least one computing device and includes: receiving at least one query from a user device; determining a context for the at least one query, selecting at least one deep learning network (DLN) of a plurality of DLNs to process the at least one query, where the selecting is based at least on matching the context to the at least one DLN, sending at least a representation of the at least one query and the context to the at least one DLN, receiving at least one response to the at least one query from the at least one DLN, and sending the at least one response to the user device.