Deep Classification Model Reparameterization for New Class Categorization
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Solution Overview
Problem
Deep learning algorithms face scalability issues in new class categorization due to the limited availability of annotated data, limiting their ability to effectively classify images from new classes with only a small amount of information.
Innovation Solution
The method involves decoupling the feature extraction part from the classifier part of a deep classification model by reparametrizing learnable weights, training the feature extraction part as a multiclass classification task, and episodically training the classifier part to generate classification weights using limited exemplar class information, enabling the model to adapt to new classes with scarce data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If deep learning algorithms are trained with limited annotated data for new classes, then the model can adapt to new classes, but the classification accuracy and discriminative capability deteriorate
Solution Approach 1:
The classification model is segmented into two independent parts: a feature extraction part trained on abundant data and a classification part trained episodically on limited new class data. This segmentation allows each part to be optimized for its specific function, maintaining high classification accuracy while adapting to new classes.
Solution Approach 2:
The feature extraction part is trained in advance on abundant annotated data from source classes before encountering new classes. This preliminary training ensures that the feature extractor has already learned discriminative representations, so when new classes arrive with limited data, the model can immediately achieve high classification accuracy without needing to retrain the entire model.
2Adaptability or versatility
If the entire deep classification model is retrained for new classes, then the model can adapt to new classes, but the training time and computational resources increase
Solution Approach 1:
The model is divided into feature extraction and classification components that can be trained independently. The feature extraction part is trained once on abundant data, while the classification part is trained episodically on limited new class data, dramatically reducing training time compared to retraining the entire model.
Solution Approach 2:
The feature extraction part performs preliminary learning on abundant source class data, establishing a solid foundation before new classes are introduced. This preliminary action eliminates the need to retrain the computationally intensive feature extraction layers when adapting to new classes, saving significant training time.
3Measurement precision
If abundant annotated data is used for training, then the classification accuracy is improved, but the scalability to new classes with limited information deteriorates
Solution Approach 1:
The training data is segmented into abundant source class data for feature extraction training and limited new class data for classification training. This segmentation allows the model to leverage abundant data for learning robust features while adapting to new classes with minimal data, achieving both high accuracy and scalability.
Solution Approach 2:
The feature extraction part learns universal, discriminative features from source classes that are transferable to new classes. This universal feature representation enables the model to achieve high classification accuracy on new classes even with limited annotated data, resolving the contradiction between accuracy and scalability.
Data Source
AI summary
A computer-implemented method and system are provided for training a model for New Class Categorization (NCC) of a test image. The method includes decoupling, by a hardware processor, a feature extraction part from a classifier part of a deep classification model by reparametrizing learnable weight variables of the classifier part as a combination of learnable variables of the feature extraction part and of a classification weight generator of the classifier part. The method further includes training, by the hardware processor, the deep classification model to obtain a trained deep classification model by (i) learning the feature extraction part as a multiclass classification task, and (ii) episodically training the classifier part by learning a classification weight generator which outputs classification weights given a training image.