Ayşegül Dündar

1.3k total citations
24 papers, 519 citations indexed

About

Ayşegül Dündar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Ayşegül Dündar has authored 24 papers receiving a total of 519 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Computational Mechanics. Recurrent topics in Ayşegül Dündar's work include Generative Adversarial Networks and Image Synthesis (8 papers), Advanced Neural Network Applications (8 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Ayşegül Dündar is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (8 papers), Advanced Neural Network Applications (8 papers) and Advanced Image and Video Retrieval Techniques (6 papers). Ayşegül Dündar collaborates with scholars based in United States, Türkiye and Germany. Ayşegül Dündar's co-authors include Eugenio Culurciello, Jonghoon Jin, Berin Martini, Vinayak Gokhale, Zhiding Yu, Ting-Chun Wang, Bryan Catanzaro, Andrew Tao, Clément Farabet and İsmail Sarıtaş and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Ayşegül Dündar

21 papers receiving 504 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ayşegül Dündar United States 11 384 227 137 56 23 24 519
Jonghoon Jin United States 6 286 0.7× 226 1.0× 111 0.8× 56 1.0× 21 0.9× 10 384
Shuanglong Liu United Kingdom 14 250 0.7× 200 0.9× 199 1.5× 57 1.0× 46 2.0× 34 486
Junbin Wang China 6 329 0.9× 277 1.2× 153 1.1× 53 0.9× 30 1.3× 8 487
Matthieu Courbariaux France 3 311 0.8× 167 0.7× 219 1.6× 38 0.7× 19 0.8× 4 436
Zhengang Li United States 13 238 0.6× 225 1.0× 181 1.3× 55 1.0× 39 1.7× 41 512
Shibin Tang China 7 361 0.9× 316 1.4× 160 1.2× 79 1.4× 42 1.8× 7 560
Lingzhi Sui China 7 493 1.3× 445 2.0× 208 1.5× 111 2.0× 44 1.9× 11 712
Tuan Nghia Nguyen South Korea 3 230 0.6× 193 0.9× 77 0.6× 28 0.5× 16 0.7× 10 360
Zhiqiang Que United Kingdom 15 238 0.6× 143 0.6× 206 1.5× 71 1.3× 29 1.3× 57 504

Countries citing papers authored by Ayşegül Dündar

Since Specialization
Citations

This map shows the geographic impact of Ayşegül Dündar'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 Ayşegül Dündar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ayşegül Dündar more than expected).

Fields of papers citing papers by Ayşegül Dündar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ayşegül Dündar. 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 Ayşegül Dündar. The network helps show where Ayşegül Dündar may publish in the future.

Co-authorship network of co-authors of Ayşegül Dündar

This figure shows the co-authorship network connecting the top 25 collaborators of Ayşegül Dündar. A scholar is included among the top collaborators of Ayşegül Dündar 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 Ayşegül Dündar. Ayşegül Dündar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Dündar, Ayşegül, et al.. (2024). Warping the Residuals for Image Editing with StyleGAN. International Journal of Computer Vision. 133(5). 2311–2326. 1 indexed citations
2.
Güdükbay, Uğur, et al.. (2024). Refining 3D Human Texture Estimation From a Single Image. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 11464–11475. 1 indexed citations
3.
Dündar, Ayşegül, et al.. (2023). Learning Portrait Drawing with Unsupervised Parts. International Journal of Computer Vision. 132(4). 1205–1218. 1 indexed citations
4.
Dündar, Ayşegül, et al.. (2023). StyleRes: Transforming the Residuals for Real Image Editing with StyleGAN. 1828–1837. 22 indexed citations
5.
Dündar, Ayşegül, et al.. (2023). Diverse Inpainting and Editing with GAN Inversion. 23063–23073. 10 indexed citations
6.
Dündar, Ayşegül, et al.. (2023). Benchmarking the Robustness of Instance Segmentation Models. IEEE Transactions on Neural Networks and Learning Systems. 35(12). 17021–17035.
7.
Dündar, Ayşegül, et al.. (2023). Image-to-Image Translation With Disentangled Latent Vectors for Face Editing. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(12). 14777–14788. 17 indexed citations
8.
Eryilmaz, Sukru Burc & Ayşegül Dündar. (2022). Understanding How Orthogonality of Parameters Improves Quantization of Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 34(12). 10737–10746. 2 indexed citations
9.
Dündar, Ayşegül, Kevin J. Shih, Ting-Chun Wang, et al.. (2022). Partial Convolution for Padding, Inpainting, and Image Synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(5). 1–15. 31 indexed citations
10.
Yu, Ning, Guilin Liu, Ayşegül Dündar, et al.. (2021). Dual Contrastive Loss and Attention for GANs. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 6711–6722. 1 indexed citations
11.
Dündar, Ayşegül, et al.. (2020). Domain Stylization: A Fast Covariance Matching Framework Towards Domain Adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 43(7). 2360–2372. 22 indexed citations
12.
Mardani, Morteza, et al.. (2020). Neural FFTs for Universal Texture Image Synthesis. Neural Information Processing Systems. 33. 14081–14092. 7 indexed citations
13.
Borji, Ali & Ayşegül Dündar. (2017). A new look at clustering through the lens of deep convolutional neural networks. arXiv (Cornell University).
14.
Dündar, Ayşegül, Jonghoon Jin, Berin Martini, & Eugenio Culurciello. (2016). Embedded Streaming Deep Neural Networks Accelerator With Applications. IEEE Transactions on Neural Networks and Learning Systems. 28(7). 1572–1583. 82 indexed citations
15.
Yaşar, Ali, İsmail Sarıtaş, Mehmet Akif Şahman, & Ayşegül Dündar. (2015). CLASSIFICATION OF LEAF TYPE USING ARTIFICIAL NEURAL NETWORKS. International Journal of Intelligent Systems and Applications in Engineering. 3(4). 136–136. 16 indexed citations
16.
Jin, Jonghoon, Ayşegül Dündar, & Eugenio Culurciello. (2014). Flattened Convolutional Neural Networks for Feedforward Acceleration. International Conference on Learning Representations. 15 indexed citations
17.
Dündar, Ayşegül, Jonghoon Jin, Vinayak Gokhale, Berin Martini, & Eugenio Culurciello. (2014). Memory access optimized routing scheme for deep networks on a mobile coprocessor. 18 indexed citations
18.
Gokhale, Vinayak, Jonghoon Jin, Ayşegül Dündar, Berin Martini, & Eugenio Culurciello. (2014). A 240 G-ops/s Mobile Coprocessor for Deep Neural Networks. 696–701. 217 indexed citations
19.
Jin, Jonghoon, et al.. (2014). An efficient implementation of deep convolutional neural networks on a mobile coprocessor. 133–136. 27 indexed citations
20.
Culurciello, Eugenio, et al.. (2013). Clustering learning for robotic vision. idUS (Universidad de Sevilla). 1 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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