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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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
Koyak, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290538 · Cited by 7,717
Wood, S. N. (2010) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2010.00749.x · Cited by 7,538
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., & Liang, J. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535302 · Cited by 3,277
Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.97 · Cited by 2,484
Smoothing Parameter and Model Selection for General Smooth Models
Wood, S., Pya, N., & Säfken, B. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2016.1180986 · Cited by 1,366
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230
Smith, L. N. (2018) · arXiv · doi:10.48550/arxiv.1803.09820 · Cited by 790
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679
Learning Data Augmentation Strategies for Object Detection
Zoph, B., Cubuk, E. D., Ghiasi, G., Lin, T.-Y., Shlens, J., & Le, Q. V. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58583-9_34 · Cited by 662
Camera traps in animal ecology : methods and analyses
O’Connell, A. F., Nichols, J. D., & Kāranta, K. U. (2011) · Cited by 615
Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C. (2015) · Scientific Data · doi:10.1038/sdata.2015.26 · Cited by 510
Machine learning to classify animal species in camera trap images: Applications in ecology
Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Di Salvo, P. A., Lewis, J. S., White, M. D., et al. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13120 · Cited by 504
Smoothing Parameter and Model Selection for General Smooth Models
Wood, S. N., Pya, N., & Säfken, B. (2016) · Figshare · doi:10.6084/m9.figshare.3405673 · Cited by 496
Identifying animal species in camera trap images using deep learning and citizen science
Willi, M., Pitman, R. T., Cardoso, A. W., Locke, C. M., Swanson, A., Boyer, A., Veldthuis, M., & Fortson, L. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13099 · Cited by 402
Rovero, F., Zimmermann, F., Berzi, D., & Meek, P. D. (2013) · Bollettino del CILEA (CILEA) · doi:10.4404/hystrix-24.2-6316 · Cited by 376
Self-citationGomez-Villa, A., Salazar, A., & Vargas‐Bonilla, J. F. (2017) · Ecological Informatics · doi:10.1016/j.ecoinf.2017.07.004 · Cited by 371
Kellenberger, B., Marcos, D., & Tuia, D. (2018) · Remote Sensing of Environment · doi:10.1016/j.rse.2018.06.028 · Cited by 335
Revisiting Unreasonable Effectiveness of Data in Deep Learning EraPreprint
Sun, C., Shrivastava, A., Singh, S., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.02968 · Cited by 300
Cho, J., Lee, K., Shin, E., Choy, G., & Do, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06348 · Cited by 268
Deep Learning on Small Datasets without Pre-Training using Cosine Loss
Barz, B., & Denzler, J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093286 · Cited by 189
The pitfalls of wildlife camera trapping as a survey tool in Australia
Meek, P. D., Ballard, G., & Fleming, P. J. S. (2015) · Australian Mammalogy · doi:10.1071/am14023 · Cited by 187
Self-citation2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
da Costa, V. G. T., Zara, G., Rota, P., Oliveira-Santos, T., Sebe, N., Murino, V., & Elisa, R. (2021) · doi:10.1109/wacv48630.2021 · Cited by 131
Underwater Fish Detection Using Deep Learning for Water Power Applications
Xu, W., & Matzner, S. (2018) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci46756.2018.00067 · Cited by 121
Synthetic Examples Improve Generalization for Rare Classes
Beery, S., Liu, Y., Morris, D., Piavis, J., Kapoor, A., Meister, M., Joshi, N., & Perona, P. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093570 · Cited by 103
The history of wildlife camera trapping as a survey tool in Australia
Meek, P. D., Ballard, G., Vernes, K., & Fleming, P. J. S. (2015) · Australian Mammalogy · doi:10.1071/am14021 · Cited by 80
Self-citationClare, J. D. J., Townsend, P. A., Anhalt‐Depies, C., Locke, C. M., Stenglein, J. L., Frett, S., Martin, K. J., SINGH, A. K., Deelen, T. R. V., & Zuckerberg, B. (2019) · Ecological Applications · doi:10.1002/eap.1849 · Cited by 71
ClassifyMe: A Field-Scouting Software for the Identification of Wildlife in Camera Trap Images
Falzon, G., Lawson, C., Cheung, K.-W., Vernes, K., Ballard, G., Fleming, P. J. S., Glen, A. S., Milne, H., Mather-Zardain, A. T., & Meek, P. D. (2019) · Animals · doi:10.3390/ani10010058 · Cited by 69
Self-citationScotson, L., Johnston, L., Iannarilli, F., Wearn, O. R., Mohd‐Azlan, J., Wong, W. M., Gray, T. N. E., Dinata, Y., Suzuki, A., Willard, C. E., et al. (2017) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.54 · Cited by 61
Recognition in Terra IncognitaPreprint
Beery, S., Horn, G. V., & Perona, P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.04975 · Cited by 52
AnimalFinder: A semi-automated system for animal detection in time-lapse camera trap images
Tack, J. L. P., West, B. S., McGowan, C. P., Ditchkoff, S. S., Reeves, S. J., Keever, A. C., & Grand, J. B. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.11.003 · Cited by 51
Robust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant Embeddings
Moskvyak, O., Maire, F., Dayoub, F., Armstrong, A. O., & Baktashmotlagh, M. (2021) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta52665.2021.9647359 · Cited by 48
WiseEye: Next Generation Expandable and Programmable Camera Trap Platform for Wildlife Research
Nazir, S., Newey, S., Irvine, R. J., Verdicchio, F., Davidson, P., Fairhurst, G., & van der Wal, R. (2017) · PLoS ONE · doi:10.1371/journal.pone.0169758 · Cited by 45
Camera Trapping Technology and Related Advances: into the New Millennium
Meek, P. D., Ballard, G., Falzon, G., Williamson, J., Milne, H., Farrell, R., Stover, J., Mather-Zardain, A. T., Bishop, J. C., Cheung, K.-W., et al. (2019) · Australian Zoologist · doi:10.7882/az.2019.035 · Cited by 21
Self-citationRobust Re-identification of Manta Rays from Natural Markings by Learning Pose Invariant EmbeddingsPreprint
Moskvyak, O., Maire, F., Armstrong, A. O., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10847 · Cited by 11
Computer-assisted identification of small Australian mammals in camera trap imagery
Falzon, G., Meek, P. D., & Vernes, K. (2014) · EPubDev Repository · Cited by 8
Self-citation
Research works printed but not linked: 1
- Swann, 2014, Camera trapping for animal monitoring and management: a review of applications, 4
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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
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