Classification Model Retraining via Data Reweighting
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Solution Overview
Problem
Classification models trained on labeled data may perform sub-optimally when faced with unlabeled data from different distributions, leading to unpredictable performance due to differences in speech characteristics and environments between training and test data in automatic speech recognition systems.
Innovation Solution
The method involves reweighting labeled training data based on similarities with unlabeled input data to retrain the classification model, using techniques such as Gaussian Mixture Models to identify similar features and adjust weight values, and building a similarity model to select subsets of labeled data that match the unlabeled data, thereby improving model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a classification model is trained on labeled training data, then the model can be constructed with known class associations, but the model performs sub-optimally when faced with unlabeled data from different distributions
Solution Approach 1:
The patent applies preliminary action by computing importance weights for training data samples before the actual training process. The system calculates similarity metrics between training data and unlabeled test data in advance, uses these to determine importance weights, and then reweights the training data accordingly. This preliminary reweighting ensures the model is better adapted to the test distribution from the start, rather than dealing with distribution mismatch during or after training.
Solution Approach 2:
The patent implements parameter changes by modifying the weights assigned to training data samples. Instead of treating all training samples equally, the system changes the parameter (weight) of each sample based on its importance relative to the test distribution. This is achieved through computing similarity metrics and deriving importance weights that reflect how representative each training sample is of the test data distribution.
2Ease of manufacture
If standard training methods are used with uniform weights, then the training process is simple, but the model fails to account for distribution differences between training and test data
Solution Approach 1:
The patent changes the parameter of uniform weighting to variable weighting based on data importance. Instead of using equal weights for all training samples, the system computes importance weights that reflect the representativeness of each sample relative to the test distribution. This parameter change maintains computational feasibility while significantly improving classification accuracy on test data from different distributions.
Solution Approach 2:
The patent substitutes the simple mechanical approach of uniform sampling with a more sophisticated system based on similarity computation and importance weighting. Rather than mechanically selecting random samples or treating all samples equally, the system uses computational metrics (similarity scores, importance weights) to intelligently select and weight training samples, replacing the brute-force uniform approach with a more refined computational mechanism.
3Quantity of substance
If all labeled training data is used with equal weight, then the training data is fully utilized, but samples that are less similar to test data contribute equally to training
Solution Approach 1:
The patent applies local quality by assigning different weights to different training samples based on their local characteristics (similarity to test data). Instead of treating all training data uniformly, the system identifies samples that are locally more representative of the test distribution and gives them higher weights. This allows the training process to focus on the most relevant portions of the training data while still utilizing the full dataset.
Solution Approach 2:
The patent changes the weight parameter of training samples from uniform to variable based on importance metrics. By computing similarity between training samples and unlabeled test data, the system derives importance weights that modify the contribution of each sample to the training objective. This parameter change ensures that samples more similar to the test distribution have greater influence on the learned model.
Data Source
AI summary
Techniques for evaluation and/or retraining of a classification model built using labeled training data. In some aspects, a classification model having a first set of weights is retrained by using unlabeled input to reweight the labeled training data to have a second set of weights, and by retraining the classification model using the labeled training data weighted according to the second set of weights. In some aspects, a classification model is evaluated by building a similarity model that represents similarities between unlabeled input and the labeled training data and using the similarity model to evaluate the labeled training data to identify a subset of the plurality of items of labeled training data that is more similar to the unlabeled input than a remainder of the labeled training data.