Neural Network Attention Mechanism for Dynamic Comparison Set Classification
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Solution Overview
Problem
Existing neural networks struggle to effectively classify new data examples using comparison sets that are different from the training data sets, requiring re-training or significant modifications.
Innovation Solution
A method utilizing a neural network attention mechanism to determine attention weights for comparison examples, allowing the classification of new examples based on a comparison set with respective label vectors, without the need for re-training the neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a neural network is trained on a specific training data set, then it achieves good classification accuracy on that data, but it struggles to effectively classify new data examples using comparison sets that are different from the training data sets
Solution Approach 1:
The system pre-computes and stores embedding representations for comparison examples in a comparison set database before they are needed for classification. This preliminary action allows the neural network to efficiently retrieve and compare embeddings during inference without requiring re-training, enabling adaptation to different comparison sets while maintaining classification accuracy
Solution Approach 2:
The patent introduces embedding representations as an intermediary between the training data and the comparison sets. These embeddings serve as a bridge that allows the neural network to generalize across different data distributions without re-training, as the embeddings capture the essential features in a standardized form that can be compared across different sets
2Adaptability or versatility
If re-training or significant modifications are made to handle different comparison sets, then adaptability improves, but time consumption and computational resources increase
Solution Approach 1:
By pre-computing and storing embedding representations for comparison examples before they are needed for classification, the system eliminates the need for time-consuming re-training processes. This preliminary action allows the neural network to efficiently adapt to different comparison sets by simply retrieving pre-processed embeddings during inference
Solution Approach 2:
The patent creates embedding representations as copies or simplified versions of the original comparison examples. These embedding copies capture the essential features needed for classification while requiring significantly less computational resources to process, allowing rapid adaptation without re-training the full neural network
Data Source
AI summary
Methods, systems, and apparatus for classifying a new example using a comparison set of comparison examples. One method includes maintaining a comparison set, the comparison set including comparison examples and a respective label vector for each of the comparison examples, each label vector including a respective score for each label in a predetermined set of labels; receiving a new example; determining a respective attention weight for each comparison example by applying a neural network attention mechanism to the new example and to the comparison examples; and generating a respective label score for each label in the predetermined set of labels from, for each of the comparison examples, the respective attention weight for the comparison example and the respective label vector for the comparison example, in which the respective label score for each of the labels represents a likelihood that the label is a correct label for the new example.