Debiasing Network for Low-Power Feature Bias Removal
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional feature extractors trained on specific datasets often produce biased results when applied to real-world data, especially when the data differs in sensor types or demographics, leading to increased error rates in classification and regression tasks, which is challenging for devices with limited processing power to address without re-training.
Innovation Solution
A debiasing network is introduced to refine feature extraction by receiving basic features from a pre-trained feature extractor, allowing adaptation to real-world data without modifying the extractor, using a lower dimensionality structure that can be trained on devices with limited processing power, and operating online to reduce bias without requiring additional training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the feature extractor is re-trained on real-world data to remove bias, then classification accuracy is improved, but processing power requirements increase significantly
Solution Approach 1:
The system segments the bias removal task from the feature extractor by introducing a separate debiasing network that operates independently on the extracted features. This allows the feature extractor to remain unchanged and pre-trained, while the debiasing network handles adaptation to real-world data with minimal processing power requirements.
Solution Approach 2:
The debiasing network acts as an intermediary component between the pre-trained feature extractor and the classification system. It receives basic features from the extractor, removes bias through learned transformations, and outputs debiased features that maintain compatibility with existing classification pipelines.
2Reliability
If the feature extractor is re-trained to adapt to real-world data, then bias is reduced, but device complexity increases
Solution Approach 1:
The system separates concerns by dividing the feature processing pipeline into distinct components: the pre-trained feature extractor for general feature extraction and the debiasing network for specific bias removal. This modular approach reduces overall system complexity compared to re-training the entire feature extractor.
Solution Approach 2:
Instead of re-training the entire feature extractor system, the solution applies partial action by only training the debiasing network component. This selective training approach reduces complexity while achieving the desired bias removal effect on real-world data.
3Measurement precision
If additional training data is collected and used for re-training, then classification accuracy improves, but loss of time increases
Solution Approach 1:
The feature extractor is pre-trained on comprehensive datasets in advance, performing the heavy lifting of learning general features beforehand. During deployment, the debiasing network only requires minimal adaptation to real-world data, significantly reducing the time needed for data collection and training while maintaining high accuracy.
4Reliability
If the debiasing network uses high dimensionality to capture all features, then bias removal effectiveness improves, but processing power requirements increase
Solution Approach 1:
The debiasing network applies partial action by focusing only on removing bias from the extracted features rather than processing the full high-dimensional input data. This selective approach maintains effectiveness in bias removal while significantly reducing processing power requirements compared to operating on the complete feature space.
Data Source
AI summary
The present disclosure relates to a system for refining feature extraction. The system comprises a pre-trained feature extractor configured to receive input data and extract a basic feature from the received data, a debiasing network configured to receive the basic feature as an input and to extract a debiased feature therefrom, and a classifier configured to classify the debiased feature and to output a classification result. The debiasing network has an input dimensionality corresponding to the basic feature and an output dimensionality that is equal to the input dimensionality.