Synthetic Image Generation

Synthetic image generation is a research field and methodological approach concerned with the computational creation of visual data that does not originate from physical sensors. It investigates algorithmic techniques and generative models for producing images with specified characteristics or distributions, holding significant importance for applications such as data augmentation, simulation, and the development of novel visual content.

9.4K

Publications

924.2K

Citations

25K

Authors

3.2K

Institutions

Publications per year

2017–2026

8.8K

Parents

Optical Metrology

Publications

9.4K

Research papers and scholarly works on Synthetic Image Generation.

		Year	Citations
1	Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks Jun-Yan Zhu, Taesung Park, Phillip Isola, EngineeringMachine LearningImage-to-image TranslationStyle Transfer	2017	21.3K
2	Generative adversarial networks Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, · Communications of the ACM Artificial IntelligenceEngineeringMachine LearningData Science	2020	12.7K
3	High-Resolution Image Synthesis with Latent Diffusion Models Robin Rombach, Andreas Blattmann, Dominik Lorenz, · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) EngineeringMachine LearningLatent Diffusion ModelsAutoencoders	2022	12.1K
4	Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszár, Image AnalysisMachine LearningEngineeringGenerative Adversarial Network	2017	12K
5	A survey on Image Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data Convolutional Neural NetworkEngineeringMachine LearningAutoencoders	2019	11.6K

1–5 of 9.4K

Authors

25K

Leading researchers in Synthetic Image Generation. Counts cover only their work on this concept, not their overall record.

		Publications	Citations	H-Index
1	DC Daniel Cohen‐Or Tel Aviv University	57	7K	36
2	ES Eli Shechtman Adobe Systems (United States)	53	19.6K	35
3	JZ Jun-Yan Zhu Adobe Systems (United States)	47	37.9K	35
4	AA Alexei A. Efros University of California, Berkeley	44	50.4K	33
5	MY Ming–Hsuan Yang University of California, Merced	44	6.3K	32

1–5 of 25K

Institutions

3.2K

Leading universities and research organizations in Synthetic Image Generation. Counts cover only their work on this concept, not their overall record.

		Publications	Citations	H-Index
1	Google (United States) Mountain View, United States	835	182.6K	93
2	Adobe Systems (United States) San Jose, United States	528	93.8K	88
3	Tsinghua University Beijing, China	661	54.4K	70
4	University of California, Berkeley Berkeley, United States	310	254.2K	69
5	Chinese Academy of Sciences Beijing, China	872	51.4K	67

1–5 of 3.2K

Venues

Leading journals and conferences in Synthetic Image Generation. Counts cover only their publications on this concept, not their overall record.

		Publications	Citations	H-Index
1	arXiv (Cornell University)	2K	190.8K	180
2	ACM Transactions on Graphics	261	36K	94
3	2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)	193	37.6K	76
4	Proceedings of the AAAI Conference on Artificial Intelligence	199	12.6K	59
5	IEEE Transactions on Image Processing	162	17K	57

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