Meta-Ordinal Regression for Dynamic Multi-Task Image Classification
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Solution Overview
Problem
Existing deep learning methods 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 due to the need for training multiple models on dynamically distributed data.
Innovation Solution
A method and system for Model Agnostic Meta-Ordinal Regression (MAMOR) that utilizes meta-learning and ordinal regression, incorporating a tailored loss function to handle dynamically distributed data, enabling a single model to classify multiple types of objects with minimal training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple deep learning models are trained for multiple types of objects, then classification accuracy for each object type improves, but training time and computational resources increase significantly
Solution Approach 1:
The patent combines multiple object-specific classification models into a single unified model that can handle multiple object types. The model uses a shared backbone network for feature extraction and object-specific heads for classification, allowing one model to perform the work of multiple models while reducing training time and computational overhead.
Solution Approach 2:
The patent creates a universal model architecture that can classify multiple types of objects with different numbers of classes. The model uses dynamic class mapping and adaptive pooling operations to handle variable input dimensions, enabling a single model to serve multiple object classification tasks that previously required separate specialized models.
2Measurement precision
If bespoke deep learning models are developed for each object type, then classification performance for that specific object improves, but model generalizability to other object types decreases
Solution Approach 1:
The patent designs a universal model architecture with shared feature extraction layers that can be applied across different object types. The model uses adaptive mechanisms including dynamic class mapping, learnable pooling operations, and object-specific projection layers that allow the same base model to adapt to different object classification tasks without retraining the entire network.
Solution Approach 2:
The patent segments the model into modular components: a shared backbone network for general feature extraction, object-specific attention modules for adapting to different object types, and task-specific classification heads. This segmentation allows the general features to be reused across object types while maintaining specialized capabilities for each object through the attention and head modules.
3Measurement precision
If large annotated datasets are used for training, then model accuracy improves, but data availability and annotation costs increase
Solution Approach 1:
The patent performs preliminary learning of general object features and representations using the available annotated data during the shared backbone training phase. This preliminary action extracts maximum value from limited annotated data by learning transferable features that can be reused across multiple object types, reducing the need for large amounts of annotated data for each specific object class.
Solution Approach 2:
The model uses self-supervised learning mechanisms and data augmentation techniques to generate additional training signals from the limited available data. The attention mechanisms and adaptive pooling operations enable the model to learn effective representations by leveraging the structural relationships within the data itself, reducing dependency on large annotated datasets.
Data Source
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.