Training dataset reduction on generative adversarial network
Open accessAbstract
In recent years, generative model using neural network (GAN) has become an interesting field in machine learning. However, a study that investigates the effect of reducing training dataset for GAN model has not been conducted, while it is known that collecting images for training dataset requires a lot of human labor work. In this research, series of experiments with various amount of dataset have been conducted to get the idea of how small is the amount of dataset required for a GAN to work. It has been shown that the reduction to around fifty thousand images of dataset has gained a better result than a full amount dataset. Additionally, a new evaluation method for quantifying the performance of GAN network was also proposed, which can be considered later as another evaluation method for GAN framework.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
The most prominent data quality dimension in the DL papers is amount of data, which is empirically shown to benefit performance, albeit in a saturating manner.
“The most prominent dimension is amount of data123–126,129,146–159,183–189 which is empirically shown to benefit performance, albeit in a saturating manner.”
From The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review (2024) · cites this paper as [147]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
Flagged first, then by citations
Research works printed but not linked: 10
- N. Shaker, J. Togelius, and M. J. Nelson, Procedural content generation in games. Springer, 2016.
- I. Goodfellow et al., "Generative adversarial nets," in Advances in neural information processing systems, 2014, pp. 2672-2680.
- D. Rolnick, A. Veit, S. Belongie, and N. Shavit, "Deep learning is robust to massive label noise," arXiv Prepr. arXiv1705.10694, 2017.
- A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv Prepr. arXiv1511.06434, 2015.
- R. A. Rajagede, C. K. Dewa, and others, "Recognizing Arabic letter utterance using convolutional neural network," in Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2017 18th IEEE/ACIS International Conference on, 2017, pp. 181-186.
- S. Ioffe and C. Szegedy, "Batch normalization: Accelerating deep network training by reducing internal covariate shift," arXiv Prepr. arXiv1502.03167, 2015.
- M. Abadi et al., "TensorFlow: A System for Large-Scale Machine Learning.," in OSDI, 2016, vol. 16, pp. 265-283.
- Z. Liu, P. Luo, X. Wang, and X. Tang, "Deep learning face attributes in the wild," in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3730-3738.
- F. Y. Y. Z. S. Song and A. S. J. Xiao, "Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop," arXiv Prepr. arXiv1506.03365, 2015.
- I. Goodfellow, "NIPS 2016 tutorial: Generative adversarial networks," arXiv Prepr. arXiv1701.00160, 2016.
Cited by
Sorted by citations
The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review
Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01196-4 · Cited by 169 · see citing works
- Authors
- Nuha, F. U., & Afiahayati
- Affiliations
- Universitas Gadjah Mada (ID)
- Corresponding author
- Afiahayati
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 144
- Pages
- 133-139
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2901368259
- Cited by
- 21
- References
- 1
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 3 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 1
- FWCI
- 0.60
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- generative adversarial networks, training data size, data reduction, image generation, synthetic image generation
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
