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
9.4K
Research papers and scholarly works on Synthetic Image Generation.
| Year | Citations | |
|---|---|---|
2017 | 21.3K | |
2020 | 12.7K | |
2022 | 12.1K | |
2017 | 12K | |
2019 | 11.6K |
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25K
Leading researchers in Synthetic Image Generation. Counts cover only their work on this concept, not their overall record.
| Publications | Citations | H-Index | |
|---|---|---|---|
DC Tel Aviv University | 57 | 7K | 36 |
ES Adobe Systems (United States) | 53 | 19.6K | 35 |
JZ Adobe Systems (United States) | 47 | 37.9K | 35 |
AA University of California, Berkeley | 44 | 50.4K | 33 |
MY University of California, Merced | 44 | 6.3K | 32 |
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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 | |
|---|---|---|---|
Mountain View, United States | 835 | 182.6K | 93 |
San Jose, United States | 528 | 93.8K | 88 |
Beijing, China | 661 | 54.4K | 70 |
Berkeley, United States | 310 | 254.2K | 69 |
![]() Beijing, China | 872 | 51.4K | 67 |
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Leading journals and conferences in Synthetic Image Generation. Counts cover only their publications on this concept, not their overall record.
| Publications | Citations | H-Index | |
|---|---|---|---|
2K | 190.8K | 180 | |
261 | 36K | 94 | |
193 | 37.6K | 76 | |
199 | 12.6K | 59 | |
162 | 17K | 57 |
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