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Novelty detection and neural network validation
Abstract
One of the key factors which limits the use of neural networks in many industrial applications has been the difficulty of demonstrating that a trained network will continue to generate reliable outputs once it is in routine use. An important potential source of errors is novel input data; that is, input data which differ significantly from the data used to train the network. The author investigates the relationship between the degree of novelty of input data and the corresponding reliability of the outputs from the network. He describes a quantitative procedure for assessing novelty, and demonstrates its performance by using an application which involves monitoring oil flow in multiphase pipelines.
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Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., & Snoek, J. (2019) · arXiv · doi:10.48550/arxiv.1906.02530 · Cited by 989
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
Parzen-window network intrusion detectors
Yeung, D.-Y., & Chow, C. (2003) · Object recognition supported by user interaction for service robots · doi:10.1109/icpr.2002.1047476 · Cited by 277
Metadata
- Authors
- Bishop, C. M.
- Venue
- IEE Proceedings - Vision, Image, and Signal Processing
- Published
- 1994
- Volume
- 141
- Issue
- 4
- Pages
- 217
- ISSN
- 1350-245X (ISSN-L)
- OpenAlex
- W4240338048
- Cited by
- 394
- References
- 0
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Bishop, C. M. (1994). Novelty detection and neural network validation. IEE Proceedings - Vision, Image, and Signal Processing. https://doi.org/10.1049/ip-vis%3A19941330
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
