Zero-Shot Learning Prediction-Correction Cluster Center Adjustment
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Solution Overview
Problem
Classifiers developed using traditional deep learning methods struggle to generalize and classify images from new classes without requiring retraining, limiting their ability to extrapolate beyond initial training data.
Innovation Solution
A prediction-correction approach is employed, where the training data is partitioned into prediction and correction sets, allowing a deep learning system to use class descriptions to predict and correct cluster centers for new classes, enabling zero-shot learning without retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional deep learning training methods are used with predetermined training data, then the classifier can accurately classify images from trained classes, but it cannot classify images from new classes without retraining
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning system on a source domain with predetermined training data before it needs to classify target domain data. The system performs preliminary feature extraction and representation learning on source classes, then uses this pre-learned knowledge to classify target classes without additional training. This allows the classifier to handle new classes while maintaining reasonable accuracy through transfer learning mechanisms.
2Adaptability or versatility
If the classifier is retrained to recognize additional classes, then it can classify new classes, but the system requires time-consuming retraining and cannot extrapolate from initial training
Solution Approach 1:
The patent implements self-service by enabling the classifier to automatically adapt to new classes through zero-shot learning without requiring manual retraining. The system uses pre-learned feature representations and class descriptions (such as attribute vectors or natural language descriptions) to automatically classify new classes. This self-adaptive mechanism eliminates the need for time-consuming retraining processes while maintaining the ability to recognize additional classes.
3Adaptability or versatility
If the deep learning system is designed to generalize to new classes, then it can extrapolate from initial training, but traditional methods do not provide sufficient extrapolation capability
Solution Approach 1:
The patent uses an intermediary approach by introducing class description vectors or attribute representations as mediators between the visual features and class labels. Instead of directly mapping images to class labels, the system uses these intermediate representations (such as attribute vectors describing class characteristics) to bridge the gap. This intermediary layer enables better generalization to new classes while maintaining classification precision through the structured knowledge provided by the descriptions.
Data Source
AI summary
Approaches to zero-shot learning include partitioning training data into first and second sets according to classes assigned to the training data, training a prediction module based on the first set to predict a cluster center based on a class label, training a correction module based on the second set and each of the class labels in the first set to generate a correction to a cluster center predicted by the prediction module, presenting a new class label for a new class to the prediction module to predict a new cluster center, presenting the new class label, the predicted new cluster center, and each of the class labels in the first set to the correction module to generate a correction for the predicted new cluster center, augmenting a classifier based on the corrected cluster center for the new class, and classifying input data into the new class using the classifier.