Semi-labeled Training Loss Distribution Regularization
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Solution Overview
Problem
Machine learning models face challenges in accurately predicting labels when trained with incomplete or imbalanced data, where some labels are easier to obtain than others, leading to inaccurate and inefficient model training.
Innovation Solution
The method involves using a semi-labeled set of training samples with known and unknown labels, determining two loss components: a first loss component that penalizes errors in labeled samples and a second loss component that adjusts based on the distribution of predicted values compared to an expected overall distribution, to train the model effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If training data is heavily imbalanced in favor of easily obtained labels, then data collection becomes efficient and quick, but model prediction accuracy deteriorates
Solution Approach 1:
The patent modifies the loss function parameters by introducing a distribution regularization term that penalizes deviations from expected label distributions. This changes the optimization parameters to account for imbalanced data while maintaining training efficiency on easily obtainable labels.
Solution Approach 2:
The patent implements feedback by comparing the predicted label distribution against the expected overall distribution and using this discrepancy to adjust the loss function. This feedback mechanism guides the model to correct systematic biases introduced by imbalanced training data.
2Device complexity
If traditional loss functions are used on semi-labeled data, then training computation remains simple, but model accuracy deteriorates due to ignoring label distribution
Solution Approach 1:
The patent segments the loss function into two distinct components: a traditional loss term for individual sample prediction and a distribution regularization term for overall label distribution. This segmentation allows the model to address both individual accuracy and global distribution balance without excessive complexity.
Solution Approach 2:
The patent creates a composite loss function that combines traditional prediction loss with distribution regularization. This composite approach integrates multiple objectives (individual sample accuracy and overall distribution balance) into a unified training framework.
3Ease of operation
If all unlabeled samples are assumed to have the same distribution, then training process becomes straightforward, but prediction accuracy for rare labels deteriorates
Solution Approach 1:
The patent performs preliminary action by incorporating prior knowledge about the expected overall label distribution into the training process before seeing the actual labeled data. This预先设定的分布信息 guides the model to maintain appropriate class balances even for rare labels with limited training examples.
Data Source
AI summary
Systems and methods for training machine learning models are disclosed. An example method includes receiving a semi-labeled set of training samples including a first set of training samples, where each training sample in the first set is assigned a known label, and a second set of training samples, where each training sample in the second set has an unknown label, determining a first loss component, the first loss component providing a loss associated with the first set, determining a second loss component, the second loss component having a value which increases based on a difference between a distribution of individually predicted values of at least the second set and an expected overall distribution of at least the second set, and training the machine learning model, based on the first loss component and the second loss component, to predict labels for unlabeled input data.