Slice-Based Model Validation for Targeted ML Retraining
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Solution Overview
Problem
The iterative process of optimizing machine learning models for specific data slices is time-consuming and computationally expensive, requiring multiple training iterations from scratch, which is inefficient for applications like autonomous driving and credit scoring.
Innovation Solution
Generalized slice boosting methods are applied to train shallow learning models to estimate the performance impact of optimizing a machine learning model for specific data slices, providing rapid analysis and trade-off determination across different data modalities and tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple models are trained from scratch to optimize for different data slices, then model performance on specific conditions is improved, but development time and computational cost increase significantly
Solution Approach 1:
The patent segments the validation dataset into multiple data slices based on different conditions (e.g., environmental factors, object types). Instead of training separate models for each slice, the system evaluates the principal model on each slice independently, identifying which slices require optimization. This segmentation allows targeted retraining only on necessary slices rather than training multiple complete models from scratch.
Solution Approach 2:
The patent implements a preliminary evaluation step where the principal model is assessed on multiple data slices before any retraining occurs. This preliminary action identifies which specific slices have performance deficiencies, allowing the system to prioritize retraining efforts on only those slices that need improvement, rather than retraining the entire model uniformly across all conditions.
2Reliability
If multiple models are trained from scratch to optimize for different data slices, then model performance on specific conditions is improved, but computational cost increases significantly
Solution Approach 1:
The patent extracts and focuses computational resources only on the specific data slices that require optimization. By identifying underperforming slices through preliminary evaluation, the system extracts the subset of training data that needs improvement and directs retraining efforts exclusively to those areas, rather than computationally expensive full-model retraining across the entire dataset.
Solution Approach 2:
The patent changes the training parameters by adjusting the scope and focus of retraining based on slice-specific performance metrics. Instead of using fixed full-model retraining parameters, the system dynamically adjusts training parameters to match the specific needs of each data slice, optimizing computational efficiency while maintaining performance improvements.
3Reliability
If the entire model is retrained to improve performance on one data slice, then performance on that slice is improved, but performance on other slices may deteriorate
Solution Approach 1:
The patent applies local quality optimization by focusing retraining efforts on specific data slices rather than applying uniform retraining across all data. Each data slice receives targeted optimization based on its specific performance deficiencies, allowing local performance improvements without compromising the model's overall adaptability to different conditions.
Solution Approach 2:
The patent implements partial action by performing retraining only on the necessary subset of data slices that require improvement, rather than applying excessive full-model retraining to all slices. This partial approach maintains computational efficiency while preventing performance deterioration on slices that already meet performance thresholds.
Data Source
AI summary
Methods for a machine-learning network that provide efficient, scalable, and granular analyses during validation of a machine learning model are disclosed. Providing quantitative analysis information to machine learning experts when they are deciding how to proceed with further optimizing their given machine learning model allows for more directed procedures during edge case detection. Following the execution of a principal machine learning model using a validation dataset, a shallow learning model may be trained to provide simulations about how the principal model may be improved, or not, given different re-training scenarios. By using slice-based schemes, the validation dataset is divided into certain problematic data slices, and then, during inference of the shallow learning model, additional quantitative information about the effect on other data slices given a subsequent re-training of the principal model using a certain problematic data slice allows the ML expert to make more informed decisions.