Neural Network Training Data Selection via Neuron Activation Novelty
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep neural networks face challenges in maintaining performance over time due to changing real-world data distributions and the difficulty in selecting and updating training data effectively, especially in mission-critical applications like autonomous cars, where the quality of labeled training data is crucial for supervised or semi-supervised learning.
Innovation Solution
A novelty-based machine learning system that examines neuron activations in a neural network model, creates a statistical profile, measures the novelty of new data samples, and iteratively adds the most novel samples to the training data until the novelty drops below a threshold, thereby improving the model's robustness and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If random sampling methods are used to select training data, then the data selection process is simple and fast, but the training data may not capture all new types of data observed in real-world use, leading to imperfect training datasets
Solution Approach 1:
The system implements feedback by measuring neuron activation novelty and using this information to guide selective data sampling. The novelty measurement provides feedback about which data samples contain new information patterns, allowing the system to iteratively improve training data quality by focusing on high-novelty samples rather than random selection
Solution Approach 2:
The system changes the parameter selection criteria from random sampling to novelty-based sampling. By measuring neuron activation patterns and calculating novelty metrics, the system dynamically identifies and selects data samples with the highest informational value, transforming the data selection process from stochastic to deterministic based on actual model needs
2Measurement precision
If more new data samples are added to improve model performance, then the model accuracy improves, but the cost of labeling and processing increases
Solution Approach 1:
The system extracts only the most valuable data samples by measuring novelty through neuron activation patterns. Instead of using all available data or random samples, the system identifies and extracts specifically those samples with highest novelty scores, concentrating computational resources on the most informative subset of data
Solution Approach 2:
The system applies different quality standards to different data samples based on their novelty measurements. High-novelty samples receive priority for labeling and inclusion in training sets, while low-novelty samples are deprioritized. This local quality approach ensures optimal use of labeling resources on data that provides maximum benefit
3Adaptability or versatility
If the training data is regularly enhanced by sampling from real use, then the model can adapt to changing data distributions, but random sampling may fail to capture corner cases and new data types
Solution Approach 1:
The novelty measurement mechanism provides continuous feedback about which data samples represent new patterns or corner cases. By monitoring neuron activation deviations from the statistical profile, the system automatically identifies samples that differ significantly from training data distributions, ensuring corner cases are captured in subsequent training iterations
Solution Approach 2:
The system performs preliminary analysis of data samples by measuring their novelty against the trained model's statistical profile before selecting them for inclusion. This preliminary screening ensures that only samples with high informational value and potential corner case representation are selected for expensive labeling and training processes
Data Source
AI summary
The present disclosure provides a system for improving performance of a neural network model. The system receives the neural network model and a training data associated with the neural network model. In addition, the system examines a first plurality of neuron activations inside the neural network model for the training data. The system examines the first plurality of neurons for creating a statistical profile of the first plurality of neuron activations. Further, the system receives a new set of data samples to improve the neural network model. Furthermore, the system examines a second plurality of neuron activations of each new sample of the new set of data samples. Moreover, the system extracts one or more data samples from the new set of data samples with largest novelty measurements. Also, the system adds the extracted one or more samples to the training data for re-training of the neural network model.