Head Machine Learning Model Seeding with Root Model Feature Vectors
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Solution Overview
Problem
Training machine learning models for image classification is challenging due to high dimensionality, leading to lower performance when new data is presented, especially in retail environments where different stores use unique item identifiers, making it difficult to achieve granular item identification.
Innovation Solution
A system that uses a root machine learning model to produce coarse grain feature vectors, which are used to seed and maintain head machine learning models, allowing for dynamic selection and updating of head models based on transaction information and localized metadata, enabling accurate item identification without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a root machine learning model is trained on a large set of training images to achieve high accuracy, then the model performance on new data deteriorates due to high dimensionality
Solution Approach 1:
The system divides the machine learning model into two components: a root model that handles general image classification and a head model that handles store-specific item identification. This segmentation allows each model to focus on specific tasks, preventing the root model from overfitting to store-specific patterns while enabling accurate item identification through the head model's localized metadata.
Solution Approach 2:
The head model incorporates localized metadata specific to each store's item identifiers while using the root model's general feature extraction. This local quality approach allows the system to maintain accurate item identification for each specific store without requiring the root model to be retrained for each store's unique identifier system.
2Measurement precision
If head models are trained for each store with unique item identifiers to achieve granular item identification, then the training complexity and data requirements increase significantly
Solution Approach 1:
The root model serves multiple stores universally by learning general image features that are independent of store-specific identifiers. The head model then adapts to each store's specific item identifiers using localized metadata. This universal approach allows the same root model to serve multiple stores without requiring separate training for each store's unique identifier system.
Solution Approach 2:
The system performs preliminary action by extracting and storing localized metadata for each store's item identifiers in the head model before actual item identification occurs. This pre-stored metadata enables the head model to quickly and accurately identify items for each store without requiring complex real-time training or adaptation.
3Measurement precision
If models are continuously updated with real-time data to maintain accuracy, then the computational resources and time for retraining increase
Solution Approach 1:
The system uses feedback from actual transactions and item identification results to continuously refine the head model's localized metadata. This feedback mechanism allows the model to adapt to changing item identifiers and patterns over time without requiring complete retraining of the root model, thus maintaining accuracy while minimizing retraining time.
Data Source
AI summary
A root machine learning model receives an item image captured of an item at a terminal and produces output, which is associated with an item classification for the item. A head machine learning model is selected from a plurality of head models based on transaction information associated with a transaction at a terminal. A candidate item identifier is received from the terminal. The head model uses the candidate item identifier, the root model's output data, and localized metadata maintained for the head model to provide a predicted item identifier for the item. An actual item identifier for the item is received as feedback from the terminal. The localized metadata is updated with the root model's output data and the actual item identifier for the item based on the feedback.