Unified framework of Explainable AI to enhance classifier performance

R. Manjunath, B.N Chandrashekar, B. Vinutha, Rahul Arya, Arindam Chatterjee
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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.
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统一的可解释AI框架,提高分类器性能
深度学习图像分类器广泛应用于文档处理、活动监控、目标识别和分离等领域。然而,即使是最好的分类器也不是没有错误的。如果由于分类器决策而被泵入系统的错误减少,这将非常有帮助。该框架包括热图生成、属性生成、文本解释生成和激活。
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