Co-Training Ensemble Models for Open Set Accuracy
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Solution Overview
Problem
Traditional machine learning ensembles face challenges in maintaining diversity among models, especially for open-set problems, leading to degraded accuracy due to the trade-off between data fidelity and decorrelation components in loss functions.
Innovation Solution
The approach involves co-training or sequentially training machine learning models to focus on decorrelating negative results by encouraging disagreement on incorrect classes and using distilled features to ensure each model learns different input features, thereby maintaining diversity without the trade-off.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained independently using different training sets or randomized data ordering, then diversity among models is achieved, but accuracy for open-set problems degrades due to the trade-off between data fidelity and decorrelation components
Solution Approach 1:
The loss function is segmented into two distinct components: a data fidelity term that maintains accuracy on known classes, and a decorrelation term that promotes diversity among models. This segmentation allows each component to optimize its specific objective without compromising the other, resolving the trade-off between accuracy and diversity in ensemble models
Solution Approach 2:
The training approach changes the parameters by introducing a decorrelation component to the loss function that measures and penalizes similarity between model predictions. This parameter change enables the system to maintain data fidelity while actively promoting diversity through controlled parameter optimization
2Adaptability or versatility
If decorrelation component is added to loss function to increase model diversity, then diversity among ensemble models is improved, but accuracy on in-distribution data deteriorates due to the trade-off
Solution Approach 1:
The loss function is divided into separate data fidelity and decorrelation components, allowing the system to simultaneously optimize for both accuracy on known classes and diversity among models. The segmentation ensures that diversity improvement does not come at the cost of accuracy degradation
Solution Approach 2:
The decorrelation is applied selectively only to out-of-distribution samples rather than all training data. This partial action approach ensures that models maintain high accuracy on in-distribution data while developing diversity primarily through their responses to novel, unseen classes
3Reliability
If models are trained to disagree on incorrect classes, then ability to identify out-of-distribution data is improved, but training complexity increases due to the trade-off between data fidelity and decorrelation components
Solution Approach 1:
The training process is segmented into distinct objectives: one component maintains fidelity to correct labels for in-distribution data, while another component specifically targets disagreement on incorrect classes for out-of-distribution detection. This segmentation manages complexity by clearly separating the two training goals
Solution Approach 2:
The decorrelation component provides feedback during training by measuring the agreement between models on incorrect classes and adjusting predictions accordingly. This feedback mechanism enables models to learn disagreement patterns that improve out-of-distribution detection without requiring complex manual intervention
Data Source
AI summary
Described are systems and methods for training machine learning models of an ensemble of models that are de-correlated. For example, two or more machine learning models may be concurrently trained (e.g., co-trained) while adding a decorrelation component to one or both models that decreases the pairwise correlation between the outputs of the models. Unlike traditional approaches, in accordance with the disclosed implementations, only the negative results need to be decorrelated.