The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
“How many images do I need?” Understanding how sample size per class affects deep learning model performance metrics for balanced designs in autonomous wildlife monitoring
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Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“It is generally accepted that the performance of a deep learning neural network improves logarithmically with increasing sample size [74-77].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [76]
In the citing paper
“Good accuracies with reasonable classification results can be achieved with sample sizes as small as 150-500 images per class [76].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [76]
Citations
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Distilling the Knowledge in a Neural Network
Hinton, G., Vinyals, O., & Dean, J. (2015) · arXiv · doi:10.48550/arxiv.1503.02531 · Cited by 14,204
Generalized Additive Models: An Introduction with R
Wood, S. N. (2017) · doi:10.1201/9781315370279 · Cited by 13,852
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 12,575
Generalized Additive Models: An Introduction With R
Chiang, A. Y. (2007) · Technometrics · doi:10.1198/tech.2007.s505 · Cited by 8,532
Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Research works printed but not linked: 1
- Swann, 2014, Camera trapping for animal monitoring and management: a review of applications, 4
Cited by
Sorted by citations
Detection of Cardiovascular Diseases in ECG Images Using Machine Learning and Deep Learning Methods
Abubaker, M., & Babayiğit, B. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3159505 · Cited by 243
Deep learning for survival analysis: a review
Wiegrebe, S., Kopper, P., Sonabend, R., Bischl, B., & Bender, A. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10681-3 · Cited by 226
Artificial intelligence for carbon emissions using system of systems theory
Gaur, L., Afaq, A., Arora, G. K., & Khan, N. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102165 · Cited by 187
Silva, N., Zhang, D., Kulvičius, T., Gail, A., Barreiros, C., Lindstaedt, S., Kraft, M., Bölte, S., Poustka, L., Nielsen‐Saines, K., et al. (2021) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2021.103854 · Cited by 137
Fung, D. L. X., Liu, Q., Zammit, J., Leung, C. K.-S., & Hu, P. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02992-2 · Cited by 104
Metadata
- Authors
- Shahinfar, S., Meek, P., & Falzon, G.
- Affiliations
- Agriculture Victoria (AU)
- New South Wales Department of Primary Industries (AU)
- University of New England (AU)
- Corresponding author
- Shahinfar, Saleh
- Venue
- Ecological Informatics
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 57
- Pages
- 101085
- ISSN
- 1574-9541 (ISSN-L)
- OpenAlex
- W3011707748
- Cited by
- 229
- References
- 46
- Citations by year
- 2026: 27 · 2025: 53 · 2024: 42 · 2023: 44 · 2022: 41 · 2021: 21 · 2020: 1
- FWCI
- 10.40
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Ecological Informatics (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- RUNE (Research UNE) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Department of Agriculture and Water Resources, Australian Government
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
- Keywords
- camera trap images, balanced designs, generalized additive models, precision and recall, false positive rate, ResNet, DnCNN, research design
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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