The DOI matches its Crossref record; 209 of the 209 references it cites were found in a registry or an index.
On Interpretability of Artificial Neural Networks: A Survey
Open accessAbstract
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, increasing the interpretability of deep neural networks has recently attracted much research attention. In this paper, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies in improving interpretability of neural networks, describe applications of interpretability in medicine, and discuss possible future research directions of interpretability, such as in relation to fuzzy logic and brain science.
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Albahri, A. S., Duhaim, A. M., Fadhel, M. A., Alnoor, A., Baqer, N. S., Alzubaidi, L., Albahri, O. S., Alamoodi, A. H., Bai, J., Salhi, A., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.008 · Cited by 716
Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey
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A literature review on applications of explainable artificial intelligence (XAI)
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Metadata
- Authors
- Fan, F.-L., Xiong, J., Li, M., & Wang, G.
- Venue
- IEEE Transactions on Radiation and Plasma Medical Sciences
- Published
- 2021-11
- Volume
- 5
- Issue
- 6
- Pages
- 741-760
- ISSN
- 2469-7303 (ISSN-L)
- 2469-7311
- OpenAlex
- W3154435685
- Cited by
- 561
- References
- 209
- FWCI
- 32.99
- Type
- journal article
- Language
- en
- Open access
- green
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- Fan, F.-L., Xiong, J., Li, M., & Wang, G. (2021). On Interpretability of Artificial Neural Networks: A Survey. IEEE Transactions on Radiation and Plasma Medical Sciences. https://doi.org/10.1109/trpms.2021.3066428
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