Rinon Gal

2.0k total citations · 3 hit papers
20 papers, 817 citations indexed

About

Rinon Gal is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Cellular and Molecular Neuroscience. According to data from OpenAlex, Rinon Gal has authored 20 papers receiving a total of 817 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 4 papers in Computer Graphics and Computer-Aided Design and 1 paper in Cellular and Molecular Neuroscience. Recurrent topics in Rinon Gal's work include Generative Adversarial Networks and Image Synthesis (13 papers), Image Retrieval and Classification Techniques (5 papers) and Multimodal Machine Learning Applications (5 papers). Rinon Gal is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (13 papers), Image Retrieval and Classification Techniques (5 papers) and Multimodal Machine Learning Applications (5 papers). Rinon Gal collaborates with scholars based in Israel, United States and Canada. Rinon Gal's co-authors include Amit H. Bermano, Daniel Cohen‐Or, Gal Chechik, Or Patashnik, Ron Mokady, Haggai Maron, Yuval Alaluf, Omer Tov, Yuval Atzmon and Yotam Nitzan and has published in prestigious journals such as Brain, ACM Transactions on Graphics and Computer Graphics Forum.

In The Last Decade

Rinon Gal

20 papers receiving 775 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

StyleGAN-NADA

2022 269 citations Rinon Gal, Or Patashnik et al. ACM Transactions on Graphics profile →
HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing

2022 155 citations Yuval Alaluf, Omer Tov et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) profile →
Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models

2023 87 citations Rinon Gal, Yuval Atzmon et al. ACM Transactions on Graphics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Rinon Gal Israel	11	672	170	137	84	51	20	817
Omri Avrahami Israel	6	548 0.8×	169 1.0×	121 0.9×	78 0.9×	41 0.8×	9	694
Yuval Alaluf Israel	11	896 1.3×	221 1.3×	154 1.1×	164 2.0×	74 1.5×	14	1.1k
Ron Mokady Israel	9	700 1.0×	174 1.0×	134 1.0×	97 1.2×	67 1.3×	10	845
Or Patashnik Israel	9	916 1.4×	273 1.6×	155 1.1×	190 2.3×	67 1.3×	22	1.1k
Duanqing Xu China	10	582 0.9×	81 0.5×	180 1.3×	45 0.5×	67 1.3×	41	720
Yuheng Li United States	6	502 0.7×	99 0.6×	266 1.9×	50 0.6×	37 0.7×	11	772
Cengiz Öztireli Switzerland	13	461 0.7×	180 1.1×	177 1.3×	267 3.2×	19 0.4×	23	737
Chengying Gao China	11	310 0.5×	101 0.6×	125 0.9×	80 1.0×	37 0.7×	40	465
Jiemin Fang China	11	391 0.6×	89 0.5×	146 1.1×	80 1.0×	12 0.2×	20	531
Aneeshan Sain United Kingdom	16	566 0.8×	41 0.2×	181 1.3×	50 0.6×	17 0.3×	38	688

Countries citing papers authored by Rinon Gal

Since Specialization

Citations

This map shows the geographic impact of Rinon Gal's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rinon Gal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rinon Gal more than expected).

Fields of papers citing papers by Rinon Gal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rinon Gal. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rinon Gal. The network helps show where Rinon Gal may publish in the future.

Co-authorship network of co-authors of Rinon Gal

This figure shows the co-authorship network connecting the top 25 collaborators of Rinon Gal. A scholar is included among the top collaborators of Rinon Gal based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Rinon Gal. Rinon Gal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Patashnik, Or, et al.. (2025). Nested Attention: Semantic-aware Attention Values for Concept Personalization. 1–12. 2 indexed citations

2.

Gal, Rinon, Christopher D. Makinson, Moran Hausman‐Kedem, et al.. (2025). A mouse model of GRIN2D developmental and epileptic encephalopathy recapitulates the human disease. Brain. 149(1). 90–104. 1 indexed citations

3.

Avrahami, Omri, Rinon Gal, Gal Chechik, et al.. (2024). DiffUHaul: A Training-Free Method for Object Dragging in Images. 1–12. 5 indexed citations

4.

Gal, Rinon, et al.. (2024). TurboEdit: Text-Based Image Editing Using Few-Step Diffusion Models. 1–12. 4 indexed citations

5.

Gal, Rinon, et al.. (2024). Training-Free Consistent Text-to-Image Generation. ACM Transactions on Graphics. 43(4). 1–18. 15 indexed citations

6.

Patashnik, Or, et al.. (2024). Consolidating Attention Features for Multi-view Image Editing. 1–12. 2 indexed citations

7.

Gal, Rinon, Yael Vinker, Yuval Alaluf, et al.. (2024). Breathing Life Into Sketches Using Text-to-Video Priors. 4325–4336. 7 indexed citations

8.

Gal, Rinon, et al.. (2023). Key-Locked Rank One Editing for Text-to-Image Personalization. 1–11. 64 indexed citations

9.

Gal, Rinon, Yuval Atzmon, Gal Chechik, et al.. (2023). Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models. 1–10. 27 indexed citations

10.

Gal, Rinon, et al.. (2023). Encoder-based Domain Tuning for Fast Personalization of Text-to-Image Models. ACM Transactions on Graphics. 42(4). 1–13. 87 indexed citations breakdown →

11.

Bermano, Amit H., Rinon Gal, Yuval Alaluf, et al.. (2022). State‐of‐the‐Art in the Architecture, Methods and Applications of StyleGAN. Computer Graphics Forum. 41(2). 591–611. 52 indexed citations

12.

Liu, Yunzhe, Rinon Gal, Amit H. Bermano, Baoquan Chen, & Daniel Cohen‐Or. (2022). Self-Conditioned GANs for Image Editing. 1–9. 1 indexed citations

13.

Alaluf, Yuval, Omer Tov, Ron Mokady, Rinon Gal, & Amit H. Bermano. (2022). HyperStyle: StyleGAN Inversion with HyperNetworks for Real Image Editing. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 18490–18500. 155 indexed citations breakdown →

14.

Nitzan, Yotam, et al.. (2022). LARGE: Latent-Based Regression through GAN Semantics. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 19217–19227. 11 indexed citations

15.

Mokady, Ron, et al.. (2022). Stitch it in Time: GAN-Based Facial Editing of Real Videos. 1–9. 45 indexed citations

16.

Gal, Rinon, Or Patashnik, Haggai Maron, et al.. (2022). StyleGAN-NADA. ACM Transactions on Graphics. 41(4). 1–13. 269 indexed citations breakdown →

17.

Gal, Rinon, et al.. (2021). SWAGAN. ACM Transactions on Graphics. 40(4). 1–11. 2 indexed citations

18.

Gal, Rinon, et al.. (2021). SWAGAN. ACM Transactions on Graphics. 40(4). 1–11. 50 indexed citations

19.

Gal, Rinon, Amit H. Bermano, Hao Zhang, & Daniel Cohen‐Or. (2021). MRGAN: Multi-Rooted 3D Shape Representation Learning with Unsupervised Part Disentanglement. 2039–2048. 15 indexed citations

20.

Gal, Rinon, et al.. (2020). Cardinal Graph Convolution Framework for Document Information Extraction. 1–11. 3 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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