Neural Network Embedding Space for Pattern Recognition
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Solution Overview
Problem
Conventional pattern recognition systems face challenges in accurately and efficiently identifying similar images due to the need for large libraries of known patterns, leading to unreliable results and underutilization of computing power, especially when the image of interest is significantly different from the library images.
Innovation Solution
The development of novel methods for training neural networks to create an embedding space using training sets with target and unfavored documents, adjusting parameters to minimize the difference between target and favored document embeddings, and generating production embeddings through mechanisms like variational autoencoders and Mechanical Turk training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional pattern recognition systems use large libraries of previously known patterns to improve recognition accuracy, then the reliability of pattern matching improves, but the device complexity and computational requirements increase significantly
Solution Approach 1:
The patent replaces the mechanical approach of storing and comparing against large libraries of known patterns with a neural network-based system that learns feature representations automatically. The neural network processes input images and generates embeddings that capture essential patterns without requiring explicit storage of numerous reference patterns, thereby reducing device complexity while maintaining recognition reliability.
Solution Approach 2:
The patent transforms the pattern recognition problem by changing the parameter space from direct image comparison to embedding space comparison. By mapping images to lower-dimensional embedding vectors through neural networks, the system reduces the complexity of pattern matching while preserving the essential discriminative features needed for reliable recognition.
2Reliability
If conventional systems increase the number of previously known patterns to improve accuracy, then pattern recognition accuracy improves, but the loss of time for processing and searching through larger pattern libraries increases
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks to learn robust feature embeddings from training data before actual pattern recognition tasks. This pre-learning phase enables the system to quickly process and compare new images against learned patterns without requiring extensive real-time searching through large pattern libraries, thus reducing search time while maintaining accuracy.
Solution Approach 2:
The system replaces the sequential search mechanism through large pattern libraries with a neural network-based embedding approach that enables efficient similarity computation. The neural network rapidly transforms input images into embedding vectors that can be compared using simple distance metrics, dramatically reducing processing time compared to exhaustive pattern matching.
3Ease of operation
If conventional pattern recognition systems rely on matching images to previously known patterns, then the system can provide results, but the system fails when the image of interest is significantly different from library images, reducing reliability
Solution Approach 1:
The patent introduces dynamics by using neural networks that can adapt and generalize to novel images through learned feature representations. Unlike static pattern libraries that only recognize previously seen patterns, the neural network-based system dynamically processes new images and extracts relevant features, enabling reliable recognition even when images differ significantly from training examples.
Solution Approach 2:
The system changes the parameter representation from fixed pattern templates to learned embedding vectors that capture essential semantic features. This parameter transformation enables the system to recognize images based on their semantic content rather than exact pattern matching, significantly improving reliability for novel and varied images while maintaining operational simplicity.
Data Source
AI summary
The technology disclosed introduces a concept of training a neural network to create an embedding space. The neural network is trained by providing a set of K+2 training documents, each training document being represented by a training vector x, the set including a target document represented by a vector xt, a favored document represented by a vector xs, and K>1 unfavored documents represented by vectors xiu, each of the vectors including input vector elements, passing the vector representing each document set through the neural network to derive an output vectors yt, ys and yiu, each output vector including output vector elements, the neural network including adjustable parameters which dictate an amount of influence imposed on each input vector element to derive each output vector element, adjusting the parameters of the neural network to reduce a loss, which is an average over all of the output vectors yiu of [D(yt,ys)−D(yt, yiu)].