Machine Learning Model Training State Upgrade via Error Correction
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Solution Overview
Problem
Machine learning models, particularly those used for identifying characteristics in input data structures, often suffer from errors such as switched joint predictions in kinematic pose estimation, leading to inaccurate predictions and a need for improved accuracy and correction mechanisms.
Innovation Solution
A method and system for upgrading the training state of machine learning models by predicting target data elements, identifying presumably erroneous elements, determining estimated corrected elements, and using these corrections to update the model's training state, allowing for improved prediction performance without requiring the original dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning model is used for identifying characteristics in input data structures, then the model can provide predictions and support operations, but errors such as switched joint predictions occur leading to inaccurate predictions
Solution Approach 1:
The patent implements a feedback mechanism where the machine learning model's predictions are continuously evaluated against ground truth data, and the training state is updated based on prediction errors. This closed-loop feedback system allows the model to learn from its mistakes and improve prediction accuracy over time, directly addressing the reliability issue while maintaining measurement precision through iterative refinement.
Solution Approach 2:
The patent performs preliminary actions by pre-processing input data to enhance relevant features before feeding them to the machine learning model. This preliminary enhancement of input data quality helps the model make more accurate predictions from the start, reducing errors such as switched joint predictions and improving both reliability and measurement precision before the main prediction process occurs.
2Adaptability or versatility
If the entire model is fine-tuned for a specific need, then the model adapts well to specific characteristics, but the training time and computational resources increase significantly
Solution Approach 1:
The patent applies local quality by selectively fine-tuning only specific layers or parameters of the machine learning model that are most relevant to the specific task, rather than retraining the entire model. This localized adaptation approach maintains high adaptability to specific characteristics while significantly reducing training time and computational resources compared to full model fine-tuning.
Solution Approach 2:
The patent employs partial action by performing incremental updates to the model's training state using only the necessary corrections from predicted errors, rather than complete retraining. This partial updating approach achieves sufficient adaptability to specific characteristics with minimal training time investment, avoiding the excessive computational cost of full model fine-tuning while maintaining practical adaptability.
3Productivity
If the model is continuously updated with corrections, then the prediction performance improves, but the complexity of the system increases
Solution Approach 1:
The patent implements self-service by enabling the machine learning model to automatically update its own training state using prediction errors as feedback, without requiring external retraining processes. This self-updating mechanism continuously improves prediction performance while minimizing system complexity by eliminating the need for complex external training pipelines and manual model updates.
Data Source
AI summary
A method for upgrading a training state of a machine learning model is described, the machine learning model being configured for supporting a model update. The method comprises predicting a set of target data elements based on the input data structure using the machine learning model, a target data element corresponding to a respective characteristic of the input data structure, and determining, for at least one of the predicted target data elements, whether or not a respective target data element is presumably erroneous. The method further comprises determining, for each presumably erroneous target data element detected in the previous step, an estimated corrected target data element, and performing, based on at least one estimated corrected target data element, a step of updating the training state of the machine learning model.