Deep learning image classifiers are extensively used in document processing, activity monitoring, object recognition and separations etc. However, even the best classifiers are not free from errors. It would be very helpful if the errors that are pumped in to the system due to the classifier decisions are reduced. The framework comprises of heat map generation, attribute generation, text explanation generation and activation.
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Unified framework of Explainable AI to enhance classifier performance
Semantic Scholar · Computer Science · 2019
Abstract
Deep learning image classifiers are extensively used in document processing, activity monitoring, object recognition and separations etc. However, even the best classifiers are not free from errors. It would be very helpful if the errors that are pumped in to the system due to the classifier decisions are reduced. The framework comprises of heat map generation, attribute generation, text explanation generation and activation.