Symbiotic Training Coach for Synthetic Data and Learner Feedback
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing machine learning model training is resource and data intensive, particularly due to the need for substantial labeled training samples, and there is a lack of symbiotic mechanisms for integrating learner and synthesizer networks to minimize manual intervention and improve training efficiency.
Innovation Solution
A system for symbiotic autonomous training of machine learning models, utilizing a training coach to facilitate simultaneous training of learner and synthesizer networks, where the learner network provides feedback to guide the synthesizer network on data generation, reducing the need for human intervention and improving training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If synthetic data is generated to reduce manual data collection, then data collection effort is reduced, but training accuracy may be compromised due to lack of diverse real-world data
Solution Approach 1:
The patent merges the learner network and synthesizer network into a unified symbiotic training system where both networks train simultaneously and interactively. The learner network processes synthetic data while the synthesizer network uses feedback from the learner to generate more accurate synthetic data, combining the benefits of automated data generation with improved training accuracy through iterative refinement.
Solution Approach 2:
The patent implements a feedback mechanism where the learner network's performance on synthetic data is fed back to the synthesizer network. This feedback loop allows the synthesizer network to adjust its data generation parameters to produce more accurate and useful synthetic training data, thereby improving training accuracy while maintaining reduced manual effort.
2Productivity
If learner network is trained on synthetic data, then training speed increases, but the complexity of integrating synthesizer and learner networks increases
Solution Approach 1:
The patent segments the training system into distinct functional modules: a synthesizer network for generating synthetic data, a learner network for processing training data, and a feedback mechanism for coordinating between them. This segmentation allows each component to be developed and optimized independently while maintaining overall system manageability and reducing integration complexity.
Solution Approach 2:
The patent introduces a feedback mechanism as an intermediary between the synthesizer network and learner network. This intermediary coordinates the interaction between the two networks, managing the complexity of integration by providing structured communication and control signals that synchronize their training processes.
3Reliability
If manual labeling of training data is performed, then data quality improves, but time consumption and resource burden increase
Solution Approach 1:
The patent implements self-service through the synthesizer network generating synthetic training data automatically without human intervention. The system uses the learner network's own performance feedback to guide the synthesizer network in creating training data, eliminating the need for manual labeling while maintaining data quality through iterative improvement based on actual model performance.
Solution Approach 2:
The feedback mechanism in the patent allows the system to automatically improve data quality by using the learner network's performance metrics to guide the synthesizer network's data generation. This closed-loop feedback enables continuous refinement of synthetic data quality without requiring manual verification or labeling.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An approach is provided for symbiotic autonomous training of machine learning models. The approach involves, for example, receiving an output of a learner network. The learner network is configured to assign a predicted class of an object depicted in input data and predicted coordinates from which the object was captured in the input data. The input data is synthetic input data generated using a synthesizer network based on given coordinates. The approach also involves based on one or more decision criteria, performing at least one of: (1) using the input data to activate the synthesizer network to generate additional synthetic training data within the predicted class and within a threshold range of the given coordinates so that the learner network is further trained on the additional synthetic training data; or (2) causing, at least in part, a collection of additional generator ground truth data from the given coordinates so that the synthesizer network is further trained on the additional generator ground truth data.