Symbiotic Model Training Using Synthetic Data Feedback Loops
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Solution Overview
Problem
Existing machine learning model training is resource and data intensive, particularly due to the need for substantial labeled training samples, and integrating learner and synthesizer networks for accurate training without manual intervention is challenging.
Innovation Solution
A symbiotic training system where a learner network and a synthesizer network interact autonomously, with a training coach guiding the synthesizer to generate high-quality synthetic data based on the learner's feedback, reducing the need for human intervention and optimizing training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If substantial numbers of labeled training samples are collected to train the learner network, then model accuracy is improved, but resource burden and data collection effort increase significantly
Solution Approach 1:
The synthesizer network generates synthetic training data that copies the essential characteristics and patterns of real labeled data. This allows the learner network to be trained on abundant synthetic samples without the resource-intensive process of collecting and labeling equivalent real-world data, thereby improving model accuracy while reducing resource burden
Solution Approach 2:
The system implements autonomous symbiotic training where the learner network and synthesizer network automatically interact and improve each other without manual intervention. The learner provides feedback on synthetic data quality, and the synthesizer autonomously generates improved training samples, eliminating the need for manual data collection and labeling resources
2Loss of time
If synthesizer networks are used to generate synthetic training data, then manual data collection effort is reduced, but integrating learner and synthesizer networks for accurate training remains challenging
Solution Approach 1:
The patent merges the learner network and synthesizer network into a unified symbiotic training system where both networks are integrated through a common feedback loop. The learner network evaluates synthetic data quality and provides gradients that directly guide the synthesizer network's training, creating a cohesive architecture that reduces integration complexity while maintaining autonomous operation
Solution Approach 2:
The system implements a feedback mechanism where the learner network's performance on synthetic data directly influences the synthesizer network's training process. The learner provides quality assessments and gradient signals that feed back to the synthesizer, enabling automatic adjustment and improvement of synthetic data generation without complex manual intervention or integration challenges
Data Source
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.