Improving Deep Learning Transparency: Leveraging the Power of LIME Heatmap

Author:

Farhood Helia,Najafi Mohammad,Saberi Morteza

Publisher

Springer Nature Singapore

Reference26 articles.

1. Stiffler, M., Hudler, A., Lee, E., Braines, D., Mott, D., Harborne, D.: An analysis of reliability using lime with deep learning models. In: Annual Fall Meeting of the Distributed Analytics and Information Science International Technology Alliance, AFM DAIS ITA (2018)

2. Shah, S.S., Sheppard, J.W.: Evaluating explanations of convolutional neural network image classifications. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2020)

3. Communications in Computer and Information Science;L Schallner,2020

4. Cian, D., van Gemert, J., Lengyel, A.: Evaluating the performance of the lime and grad-cam explanation methods on a lego multi-label image classification task. arXiv preprint arXiv:2008.01584 (2020)

5. Lee, E., Braines, D., Stiffler, M., Hudler, A., Harborne, D.: Developing the sensitivity of lime for better machine learning explanation. In: Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, vol. 11006, pp. 349–356. SPIE (2019)

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