Meta-Model Diagnosing Computer Vision Model Performance
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Solution Overview
Problem
Conventional classification models face challenges in diagnosing and improving performance without access to the data used to generate classification results, leading to inaccurate classifications and inefficient resource use due to limitations in data access and quality.
Innovation Solution
The implementation of a meta-model that uses feature data to diagnose classification model performance issues and improve accuracy by associating image quality and object detection metrics with classification data, enabling the generation of improved data collection protocols and updated classification models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a classification model is deployed to remote devices without access to original data, then data privacy and bandwidth requirements are improved, but the ability to diagnose and improve model performance deteriorates
Solution Approach 1:
The patent introduces feature data as an intermediary between the original private data and the classification model. Remote devices extract features from private data locally, then transmit only the feature data to the management system. This mediator approach preserves data privacy while enabling performance diagnosis, as the management system receives sufficient information to train meta-models without accessing the original private data.
Solution Approach 2:
The patent creates a copy of the essential information needed for model improvement through feature extraction. Instead of transmitting or storing original private data, the system creates feature representations that capture the necessary patterns for diagnosis and model training, allowing performance improvement without compromising data privacy.
2Quantity of substance
If feature data is transmitted instead of original data, then bandwidth and storage requirements are reduced, but the completeness of information for diagnosis is limited
Solution Approach 1:
The patent transforms the data from its original form into feature representations through parameter changes. By extracting relevant features and transmitting only these transformed parameters, the system reduces data volume while preserving the essential information needed for diagnosis and model improvement, balancing bandwidth efficiency with diagnostic completeness.
3Ease of manufacture
If a management system trains classification models without access to diverse real-world data, then model deployment is simplified, but model accuracy and adaptability deteriorate
Solution Approach 1:
The patent implements a feedback mechanism where the management system receives feature data and classification results from remote devices, uses this feedback to train meta-models that identify performance issues, and iteratively improves classification models. This continuous feedback loop enables the system to adapt to diverse real-world conditions without direct access to the underlying private data, improving both accuracy and versatility.
Data Source
AI summary
Systems and methods for clustering data are disclosed. For example, a system may include one or more memory units storing instructions and one or more processors configured to execute the instructions to perform operations. The operations may include receiving, from a first client device, a classification data associated with a classification model and receiving feature data corresponding to the classification model output. The operations may include training a meta-model to predict the classification data based on the feature data and/or additional data associated with the classification data such as location data or environmental data. The operations may include generating a meta-model output based on the classification data, the feature data, and/or the additional data. The operations may include updating the classification model based on the meta-model output and transmitting the updated classification model to at least one of the first client device or a second client device.