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In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic datasets have become increasingly significant.
G. Forman, “An extensive empirical study of feature selection metrics for text classification,” Journal of Machine Learning Research
2003
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
2013
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE international conference on computer vision
2017
Earlier work this paper cites.
M. Olivecrona, T. Blaschke, O. Engkvist, and H. Chen, “Molecular de-novo design through deep reinforcement learning,” Journal of cheminformatics
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever, et al
2018
Earlier work this paper cites.
A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” in International Conference on Learning Representations
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Nie, N. Narodytska, and A. Patel, “Relgan: Relational generative adversarial networks for text generation,” in International conference on learning representations
2018
Earlier work this paper cites.
W. Jin, R. Barzilay, and T. Jaakkola, “Junction tree variational autoencoder for molecular graph generation,” 2018
2018
Earlier work this paper cites.
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D. Hirzel, R. P. Adams, and A. Aspuru-Guzik, “Automatic chemical design using a data-driven continuous representation of molecules,” ACS central science
2018
Earlier work this paper cites.
M. Favaretto, E. De Clercq, and B. S. Elger, “Big data and discrimination: perils, promises and solutions. a systematic review,” Journal of Big Data
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al
2019
Earlier work this paper cites.
X. Sun and L. Zheng, “Dissecting person re-identification from the viewpoint of viewpoint,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2019
Earlier work this paper cites.
Z. Tang, M. Naphade, S. Birchfield, J. Tremblay, W. Hodge, R. Kumar, S. Wang, and X. Yang, “Pamtri: Pose-aware multi-task learning for vehicle re-identification using highly randomized synthetic data,” in Proceedings of the IEEE/CVF International Conference on Computer Vision
2019
Earlier work this paper cites.
Q. Wang, J. Gao, W. Lin, and Y. Yuan, “Learning from synthetic data for crowd counting in the wild,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
2019
Earlier work this paper cites.
R. Torkzadehmahani, P. Kairouz, and B. Paten, “Dp-cgan: Differentially private synthetic data and label generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
2019
Earlier work this paper cites.
J. Dahmen and D. Cook, “Synsys: A synthetic data generation system for healthcare applications,” Sensors
2019
Earlier work this paper cites.
Y. Lu, Y. T. Chang, E. P. Hoffman, G. Yu, and Y. Wang, “Integrated identification of disease specific pathways using multi-omics data,” Cold Spring Harbor Laboratory
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Z. Zhou, S. Kearnes, L. Li, R. N. Zare, and P. Riley, “Optimization of molecules via deep reinforcement learning,” Scientific reports
2019
Cited alongside, same era.
J. H. Jensen, “A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space,” Chemical science
2019
Cited alongside, same era.
F. Thabtah, S. Hammoud, F. Kamalov, and A. Gonsalves, “Data imbalance in classification: Experimental evaluation,” Information Sciences
T. Fu, C. Xiao, X. Li, L. M. Glass, and J. Sun, “Mimosa: Multi-constraint molecule sampling for molecule optimization,” in Proceedings of the AAAI Conference on Artificial Intelligence
2021
Later among the works it cites.
S. Gowal, S.-A. Rebuffi, O. Wiles, F. Stimberg, D. A. Calian, and T. A. Mann, “Improving robustness using generated data,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
2022
Later among the works it cites.
C. Dewi, R.-C. Chen, Y.-T. Liu, and S.-K. Tai, “Synthetic data generation using dcgan for improved traffic sign recognition,” Neural Computing and Applications
2022
Later among the works it cites.
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2020
Cited alongside, same era.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al
2020
Cited alongside, same era.
Y. Yao, L. Zheng, X. Yang, M. Naphade, and T. Gedeon, “Simulating content consistent vehicle datasets with attribute descent,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16
2020
Cited alongside, same era.
J. Zheng, J. Zhang, J. Li, R. Tang, S. Gao, and Z. Zhou, “Structured3d: A large photo-realistic dataset for structured 3d modeling,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part IX 16
2020
Cited alongside, same era.
2020
Cited alongside, same era.
V. U. Prabhu and A. Birhane, “Large image datasets: A pyrrhic win for computer vision?,” CoRR
2020
Cited alongside, same era.
2022
Later among the works it cites.
Z. Zhao, A. Zhu, Z. Zeng, B. Veeravalli, and C. Guan, “Act-net: Asymmetric co-teacher network for semi-supervised memory-efficient medical image segmentation,” in 2022 IEEE International Conference on Image Processing (ICIP)
2022
Later among the works it cites.
T. Fu and J. Sun, “Antibody complementarity determining regions (cdrs) design using constrained energy model,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2022
Later among the works it cites.
2022
Later among the works it cites.
T. Fu, W. Gao, C. Coley, and J. Sun, “Reinforced genetic algorithm for structure-based drug design,” Advances in Neural Information Processing Systems
2022
Later among the works it cites.
T. Fu and J. Sun, “Sipf: Sampling method for inverse protein folding,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2022
Later among the works it cites.
A. Kundu, A. Tagliasacchi, A. Y. Mak, A. Stone, C. Doersch, C. Oztireli, C. Herrmann, D. Gnanapragasam, D. Duckworth, D. Rebain, et al
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Later among the works it cites.
Q. H. Nguyen, T. T. Vu, A. T. Tran, and K. Nguyen, “Dataset diffusion: Diffusion-based synthetic data generation for pixel-level semantic segmentation,” in Thirty-seventh Conference on Neural Information Processing Systems
2023
Later among the works it cites.
M. Josifoski, M. Sakota, M. Peyrard, and R. West, “Exploiting asymmetry for synthetic training data generation: SynthIE and the case of information extraction,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
2023
Later among the works it cites.
C. Whitehouse, M. Choudhury, and A. Aji, “LLM-powered data augmentation for enhanced cross-lingual performance,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
2023
Later among the works it cites.
H. Fang, B. Han, S. Zhang, S. Zhou, C. Hu, and W.-M. Ye, “Data augmentation for object detection via controllable diffusion models,” in WACV 2024
2024
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