Meta-Ordinal Regression for Dynamic Multi-Task Image Classification
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Solution Overview
Problem
Existing deep learning techniques for multi-object multi-class classification are limited by the scarcity of large annotated datasets, requiring bespoke models that are not easily generalizable and are inefficient to train, and they lack methods for utilizing dynamically distributed data.
Innovation Solution
A Model Agnostic Meta-Ordinal Regression (MAMOR) method and system that utilizes meta-learning and ordinal regression, incorporating a tailored loss function to enable multi-task multi-class image classification with minimal training data, adapting to dynamically distributed data through a Dynamic Multi-Task-Conditional Ordinal Regression (DMT-CORN) loss function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing deep learning techniques are used for multi-class classification, then classification accuracy can be achieved, but the model development becomes highly bespoke and not easily generalizable to multiple object types
Solution Approach 1:
The patent applies universality by developing a unified deep learning framework that can handle multiple object types and multi-class classification tasks through a single model architecture. The system uses a common feature extraction backbone followed by task-specific classification heads, allowing the same base model to be applied across different object categories without requiring completely bespoke models for each type.
Solution Approach 2:
The patent segments the classification problem by introducing intermediate representation layers that separate general feature extraction from specific classification tasks. The model divides the processing into shared feature extraction modules and task-specific decision modules, enabling generalizable feature learning while maintaining task accuracy.
2Reliability
If numerous deep learning models are trained for multiple object types, then classification performance can be optimized, but the training and optimization process becomes infeasible and inefficient
Solution Approach 1:
The patent merges multiple classification tasks into a single unified model that processes multiple object types simultaneously. By combining different classification objectives within one training framework using multi-task learning, the system achieves optimized performance across all tasks while avoiding the need to train and maintain separate models for each object type.
Solution Approach 2:
The unified model serves multiple classification functions through a single training process, making the system both reliable across different object types and efficient in terms of training resources required.
3Adaptability or versatility
If traditional deep learning methods are used, then models can be trained on available data, but they lack approaches for utilizing dynamically distributed training data that arrives over time
Solution Approach 1:
The patent implements dynamics by designing a training framework that can adapt to dynamically arriving data. The system uses incremental learning capabilities where the model can be continuously updated with new training data as it becomes available, rather than requiring all data to be present simultaneously. This allows the model to adapt to new object types and classes over time while maintaining performance on previously learned tasks.
Data Source
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AI summary
The conventional multi-task ordinal regression techniques require large training data and are not specifically suitable for the dynamic data distribution settings. Embodiments herein provide a method and system for a model agnostic meta-ordinal regression (MAMOR). The method utilizes meta-learning along with ordinal regression and develops a loss function tailored to a specific problem of multi-task multi-class image classification utilizing minimal training data. The disclosed method trains a MAMOR based Artificial Neural Network (ANN model) on a plurality of tasksets by utilizing a Dynamic Multi-Task-Conditional Ordinal Regression (DMT-CORN) loss function for handling the dynamic data distribution settings.