Classifier Adaptation via Transformation Parameters
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Solution Overview
Problem
Existing machine learning models face challenges in adapting to novel classes with limited training data, particularly in scenarios like autonomous driving where classes vary significantly across regions, leading to suboptimal performance due to insufficient training examples.
Innovation Solution
The system adapts a base classifier to novel classes by determining and updating both base and novel class representations using transformation parameters, allowing for a joint classifier that can classify instances across both known and unknown classes, leveraging few-shot learning techniques and meta-learning approaches to improve discriminative power and global consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning techniques are used to train classifiers, then classification accuracy is improved, but large amounts of training data are required which increases cost and complexity
Solution Approach 1:
The system performs preliminary action by pre-training a base classifier on source domain data before deployment. This allows the classifier to learn general features and patterns in advance, so that when deployed in a target domain with limited data, it can adapt quickly without requiring extensive retraining. The base classifier is prepared beforehand with general classification capabilities that can be fine-tuned with minimal target domain examples.
Solution Approach 2:
The system applies parameter changes by learning domain-specific transformation parameters that adapt the base classifier to the target domain. Instead of retraining the entire classifier, the system learns transformation parameters that adjust the base classifier's behavior to match the target domain characteristics. This allows high accuracy to be achieved with minimal training data by modifying parameters rather than restructuring the entire model.
2Adaptability or versatility
If the classifier is adapted to novel classes with few training examples, then adaptability is improved, but discrimination between overlapping classes becomes difficult
Solution Approach 1:
The system introduces an intermediary mechanism in the form of a transformation parameter learning module that bridges the gap between the base classifier and novel classes. This intermediary learns domain-specific transformations that help the base classifier adapt to novel classes while maintaining its discriminative power. The transformation parameters act as a mediator that translates the base classifier's general features into target domain-specific features, enabling both adaptability and discrimination.
Solution Approach 2:
The system segments the classification task into two distinct components: a pre-trained base classifier that handles general feature extraction and a learned transformation parameter module that handles domain-specific adaptation. This segmentation allows the base classifier to maintain its discriminative power while the transformation parameters provide adaptability to novel classes. Each component has a specialized function, and their combination achieves both goals simultaneously.
3Measurement precision
If transformation parameters are learned to adapt the base classifier, then classification accuracy on novel classes is improved, but computational resources and training time increase
Solution Approach 1:
The system applies partial action by learning only the necessary transformation parameters needed for domain adaptation rather than retraining the entire classifier. Instead of performing excessive action (full retraining), the system selectively learns only the transformation parameters that are sufficient to adapt the base classifier to the target domain. This partial learning approach achieves high accuracy on novel classes while significantly reducing training time and computational resources compared to full retraining.
Data Source
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AI summary
A system (100) is disclosed for adapting a base classifier to one or more novel classes. The base classifier classifies an instance into a base class by extracting a feature representation from the instance using a feature extractor and matching it to class representations of the base classes. The base classifier is adapted using training data for the novel classes. Class representations of the novel classes are determined based on feature representations of instances of the novel classes. The class representations of the novel and base classes are then adapted, wherein at least one class representation of a novel class is adapted based on a class representation of a base class and at least one class representation of a base class is adapted based on a class representation of a novel class. The adapted class representations of the base and novel classes are associated with the base classifier.