Two-Stage ML Training for Imbalanced Data Bias
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Solution Overview
Problem
Machine learning models trained on imbalanced datasets, such as those with long-tailed distributions, suffer from biases due to instance-based sampling, which favors high-sample-size classes, and class-balanced sampling, which may overfocus on under-represented classes, leading to suboptimal performance across all classes.
Innovation Solution
A two-stage training method involving instance-based sampling for teacher models followed by class-balanced distillation for student models, where the student models learn to predict feature representations and classifications from the teacher models, using a distillation loss term to mimic the teacher's feature extractor and classifier, while leveraging multiple teacher models for regularization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If instance-based sampling is used to train models, then models can process data efficiently, but models become biased towards high sample size classes
Solution Approach 1:
The training process is segmented into two distinct stages: a teacher model training stage using instance-based sampling for efficiency, and a student model training stage using class-balanced sampling for accuracy. This segmentation allows each stage to optimize for different objectives, resolving the contradiction between processing efficiency and accuracy on under-represented classes.
Solution Approach 2:
Teacher models serve as intermediaries that bridge the gap between efficient instance-based sampling and accurate class-balanced sampling. The teacher models first learn from instance-based sampling, then provide distilled knowledge to student models that will be trained on class-balanced data, thereby transferring the benefits of both sampling methods.
2Reliability
If class-balanced sampling is used to train models, then models can focus on under-represented classes, but models become overfocused on small sample sized classes
Solution Approach 1:
The training process is segmented into two stages where the first stage uses instance-based sampling to maintain overall performance on well-represented classes, and the second stage uses class-balanced sampling to improve under-represented classes. This segmentation prevents overfocus on small classes while still addressing their underrepresentation.
Solution Approach 2:
The teacher models perform preliminary learning using instance-based sampling, establishing a foundation of knowledge from the majority classes. This preliminary action ensures that the student models inherit strong representations from well-represented classes before the class-balanced sampling stage refines their performance on under-represented classes.
3Ease of manufacture
If models are trained on the same training dataset in the same training order, then training is simple and efficient, but models reinforce biases learned from initial data
Solution Approach 1:
The training process is segmented into two sequential stages with different sampling strategies. The first stage usesinstance-based sampling for simplicity and efficiency, while the second stage uses class-balanced sampling to mitigate biases. This segmentation maintains training simplicity while improving model fairness and generalization.
Solution Approach 2:
The sampling strategy dynamically changes from instance-based in the first stage to class-balanced in the second stage. This dynamic adjustment allows the training process to adapt its behavior based on the stage, simplifying early training while addressing bias issues in later training.
Data Source
AI summary
Class-balanced distillation can train recognition models with little to no bias even if the training dataset has a class imbalance. A two stage training process with instance sampling and class-balanced sampling can train the recognition model to recognize both head classes and tail classes. Moreover, one or more teacher classification models can be trained, and the knowledge can be distilled to a student classification model.