Hierarchical CNN Training with Skip Connections and Progressive Data Sets
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Solution Overview
Problem
Existing learning models for hierarchical image classification face efficiency decreases as the number of classes increases, leading to a decline in accuracy during complex training tasks.
Innovation Solution
A learning model configuration that includes a convolutional neural network with a main network and sub network, utilizing skip connections where higher-level classifiers connect to lower-level classifiers, and generates training data sets through random addition and deletion of elements to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of classes in each layer increases and the classification becomes more complex, then the hierarchical classification capability is improved, but the processing efficiency for training the learning model decreases
Solution Approach 1:
The patent divides the classification task into multiple hierarchical layers, where each layer handles a specific level of classification (e.g., category level, subcategory level, product level). This segmentation allows the model to process complex hierarchical classification by breaking it down into manageable steps, improving both classification capability and training efficiency through layered processing.
Solution Approach 2:
The patent employs dynamic training strategies where the learning model adapts its processing based on the complexity of the classification task. The model dynamically adjusts its behavior during training to handle increasing numbers of classes and complexity levels, maintaining processing efficiency while improving hierarchical classification capability through adaptive learning mechanisms.
2Adaptability or versatility
If the number of classes in each layer increases and the classification becomes more complex, then the hierarchical classification capability is improved, but the accuracy of the learning model decreases
Solution Approach 1:
By segmenting the classification into hierarchical layers, each layer focuses on specific classification objectives with optimized parameters. This segmentation prevents accuracy degradation by ensuring that each layer achieves high accuracy for its specific level before proceeding to more complex levels, thereby maintaining overall accuracy despite increasing classification complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms that monitor and adjust the training process based on performance at each hierarchical level. This feedback allows the model to identify and correct accuracy issues at specific levels, maintaining high classification accuracy even as the number of classes and overall complexity increase across multiple layers.
Data Source
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AI summary
An information processing apparatus includes: a generating means for generating, from a data group including training data elements given different correct labels, multiple training data sets in such a manner that the number of included training data elements changes successively, by successively selecting one or more training data elements from the data group; and a training means for repeatedly training a learning model for machine learning by applying the training data sets to the learning model.