Random Classification Model Head for Generalization
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Solution Overview
Problem
Machine learning models often suffer from overfitting, where they memorize training data noise rather than learning general patterns, leading to poor performance on new, unseen data.
Innovation Solution
A computer-implemented method and system that involves constructing an auxiliary training set with randomly assigned identification labels, and training a machine learning model with both primary and auxiliary prediction heads. The primary head predicts class labels, while the auxiliary head predicts identification labels, with the output layer configured to produce a joint prediction that is invariant to the identification labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the model learns detailed patterns from training data, then training accuracy is improved, but generalization performance deteriorates due to overfitting
Solution Approach 1:
The patent applies the 'Blessing in disguise' principle by converting the harmful effect of random label variations into a beneficial training signal. The auxiliary prediction head is trained to predict random identification labels, and this training is used to force the main network to learn features that are invariant to these random labels. This transforms the noise from harmful overfitting signals into useful regularization that improves generalization performance.
Solution Approach 2:
The auxiliary prediction head acts as an intermediary component that mediates between the main network and the training data. It processes the same input data but predicts random identification labels instead of true class labels. This intermediary mechanism enables the system to distinguish between meaningful patterns and random noise, allowing the main network to learn robust features that generalize better.
2Measurement precision
If the training dataset size is increased, then model accuracy is improved, but computational cost and complexity increase
Solution Approach 1:
The patent applies the 'Copying' principle by creating a synthetic auxiliary training set that replicates the structure of the original training data but with random identification labels. This copied dataset allows the system to learn invariance properties without requiring additional real data collection or processing, thereby improving generalization without proportionally increasing computational complexity.
Solution Approach 2:
The patent changes the label parameters in the training data from true class labels to random identification labels for the auxiliary prediction head. This parameter transformation enables the same training data to serve dual purposes: maintaining original classification accuracy while simultaneously providing regularization signals that improve generalization, thus avoiding the need to increase dataset size.
Data Source
AI summary
A method comprising: receiving a primary training set comprising annotated data samples associated with one or more classes and annotated with class labels; constructing an auxiliary training set comprising at least some of the data samples, wherein each of the data samples is assigned at random to one of a set of identification classes, and annotated with an identification label associated with the identification class; at a training stage, train a machine learning model comprising a primary and auxiliary prediction heads, by: (i) training the primary prediction head on the primary training set to predict the class, and (ii) training the auxiliary prediction head on the auxiliary training set to predict the identification class, wherein an output layer of the machine learning model is configured to output a joint prediction which predicts the class label and is invariant to the identification label.