Novel Classifier Training via Prior Knowledge Transfer
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Solution Overview
Problem
Existing object classification models require extensive training data and time to adapt to novel class objects, leading to increased training time and reduced performance due to overfitting and random effects.
Innovation Solution
A method that constructs a novel classifier model using prior knowledge from a base classifier, incorporating parameterized weight coefficients and Gaussian random vectors to expedite training and improve classification performance, especially in low-shot learning scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a novel classifier is trained from a randomly initialized state with limited training data, then the model can be applied to novel classes, but the training time increases and classification performance decreases due to overfitting and random effects
Solution Approach 1:
The patent applies preliminary action by pre-training a base classifier on abundant base class data before adapting it to novel classes. The base classifier learns useful feature representations and decision boundaries in advance, which are then transferred to the novel classifier through parameter sharing and fine-tuning. This preliminary preparation eliminates the need to start from random initialization, significantly reducing training time while maintaining adaptability to novel classes.
Solution Approach 2:
The patent utilizes parameter changes by modifying the classifier parameters strategically during the transition from base classes to novel classes. Instead of retraining all parameters from scratch, the method freezes or partially freezes base class parameters and only trains novel class-specific parameters with limited data. This selective parameter updating reduces training time and prevents overfitting while preserving the learned features from base classes.
2Adaptability or versatility
If a novel classifier is trained from a randomly initialized state with limited training data, then the model can be applied to novel classes, but classification performance decreases due to overfitting and unexpected random effects from initial values
Solution Approach 1:
The base classifier performs preliminary learning on abundant base class data, establishing reliable feature extraction capabilities and decision-making patterns. When adapting to novel classes, this pre-learned knowledge provides a stable foundation that reduces sensitivity to random initialization and prevents overfitting to limited novel class data, thereby improving classification reliability.
Solution Approach 2:
The patent applies copying by reusing the base classifier's learned parameters and feature extraction mechanisms for the novel classifier. Instead of creating a completely new model that would be vulnerable to random initialization effects, the method copies the proven base classifier architecture and parameters, then adapts them to novel classes. This copying approach ensures consistent and reliable performance by leveraging the stability of pre-learned features.
3Reliability
If extensive training data is acquired and used to train the object classification model for novel classes, then classification performance may improve, but the training process requires a lot of time and costs
Solution Approach 1:
The patent applies parameter changes by selectively updating only the necessary parameters during novel class training. The base class parameters are frozen or updated minimally, while only novel class-specific parameters are trained with limited data. This selective parameter updating achieves good classification performance without requiring extensive training data or long training times, as the majority of parameters are reused from the pre-trained base classifier.
Solution Approach 2:
The base classifier performs preliminary training on abundant base class data, pre-learning robust feature representations and general classification skills. This preliminary action transfers to the novel classifier, allowing it to achieve good performance with minimal additional training data and time. The pre-learned parameters serve as a strong starting point that reduces the training burden for novel classes.
Data Source
AI summary
Disclosed herein are a system and method for classifying novel class objects. The method of classifying novel class objects includes (a) constructing a novel classifier considering prior knowledge acquired from a base classifier, and (b) learning a parameterized weight coefficient of a novel classifier model during the training of the novel classifier.