Deep Ensemble Active Selective Prediction for Distribution Shift
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Solution Overview
Problem
Deep Neural Networks (DNNs) face performance degradation when there is a distribution shift between training and test data, leading to overfitting and reduced accuracy in applications like satellite imaging and disease outcome prediction, where weather conditions or new trends alter data distributions.
Innovation Solution
A computer-implemented method that bridges the gap between active learning and selective prediction by using a deep ensemble model to obtain unlabeled test data samples, select subsets based on confidence values, label them, fine-tune the model, and generate pseudo-labeled training data, thereby reducing the need for human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep neural network is pre-trained on source training data, then the model achieves high accuracy on training data, but performance degrades significantly when tested on data from a different distribution
Solution Approach 1:
The system performs preliminary actions by pre-training the deep neural network on source training data before encountering target domain data. This preliminary training establishes a foundation model that can be subsequently adapted to handle distribution shifts through active learning and self-training processes, allowing the model to maintain accuracy across different data distributions.
Solution Approach 2:
The system employs self-service mechanisms through active learning and self-training processes. The model automatically selects uncertain predictions, generates pseudo-labels, and performs self-training without requiring external intervention. This self-service capability enables the model to adapt to distribution shifts autonomously, improving its performance on target domain data while maintaining accuracy on source data.
2Adaptability or versatility
If active learning is used to select and label uncertain predictions, then the model adapts to new data distributions, but human annotation requirements increase
Solution Approach 1:
The system implements self-service through the self-training process where the model generates its own training data by creating pseudo-labels from uncertain predictions. This eliminates the need for human annotation for the self-training phase, allowing the model to adapt to distribution shifts autonomously. The active learning component still requires human annotation for initial uncertain predictions, but the subsequent self-training process is fully automated.
Solution Approach 2:
The system creates pseudo-labels that copy the format and structure of ground truth labels from human-annotated data. These pseudo-labels are generated by the model's own predictions and can be used for training without requiring actual human annotation. This copying mechanism allows the model to replicate the training process using synthetic data, reducing human intervention requirements while maintaining adaptation capability.
3Adaptability or versatility
If the model is fine-tuned on a subset of labeled data, then the model adapts to specific distribution characteristics, but the training process becomes more complex
Solution Approach 1:
The training process is segmented into distinct phases: pre-training on source data, active learning on uncertain predictions, fine-tuning on labeled target data, and self-training on pseudo-labeled data. This segmentation allows each phase to focus on specific distribution characteristics while managing complexity. The fine-tuning phase specifically targets distribution-specific patterns using a subset of labeled data, while the self-training phase handles the remaining adaptation automatically.
Solution Approach 2:
The fine-tuning process uses preliminary actions from the active learning phase, where uncertain predictions are selected and labeled before fine-tuning. This preliminary labeling action prepares the data in advance, making the subsequent fine-tuning process more efficient and less complex. The model leverages the preliminary selections to focus the fine-tuning on the most informative samples, reducing the overall training complexity while maintaining distribution-specific adaptation.
Data Source
AI summary
A method includes obtaining a set of unlabeled test data samples and, for each respective initial training step, determining a first average output for each unlabeled test data sample using a deep ensemble. For each round of a plurality of rounds, the method includes selecting a subset of unlabeled test data samples based on the determined first average outputs, labeling each respective unlabeled in the subset of unlabeled test data samples, fine-tuning the deep ensemble model using the subset of labeled test data samples, and determining a second average output for each unlabeled test data sample using the fine-tuned deep ensemble model. The method also includes generating, using the set of unlabeled test data samples and the determined second average outputs, a pseudo-labeled set of training data samples. The method also includes training the deep ensemble model using the pseudo-labeled set of training data samples.