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Enhancing Incremental Learning of CNN model using Filtered New Added Samples Assisted with XAI | IEEE Conference Publication | IEEE Xplore

Enhancing Incremental Learning of CNN model using Filtered New Added Samples Assisted with XAI


Abstract:

In the incremental learning technique, the accuracy of the model is improved by increasing the number of training samples. However, there are some problems when making th...Show More

Abstract:

In the incremental learning technique, the accuracy of the model is improved by increasing the number of training samples. However, there are some problems when making this training mode. First, it is not known whether the newly added training samples will affect the learning effect of the model. Second, we know the result of the model identification, but we don't know what features are used to identify this object. In this paper, we propose a training dataset curation approach to improve model accuracy with incremental learning. To select the most appropriate explainable AI (XAI) method, we use the intersection over union (IOU) metric to measure the range calculation of real objects and model features to know the rationality of the model explanation. As we know that increase suitable training samples can improve the accuracy of the model, we added the XAI method to filter out new samples that are not suitable for the training set. We use the crack identification dataset in our method, and the accuracy can be improved from 0.895 to 0.93.
Date of Conference: 17-19 July 2023
Date Added to IEEE Xplore: 31 August 2023
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Conference Location: PingTung, Taiwan

References

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