Pinar Yanardag

1.4k total citations · 1 hit paper
15 papers, 710 citations indexed

About

Pinar Yanardag is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Pinar Yanardag has authored 15 papers receiving a total of 710 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Computer Vision and Pattern Recognition, 4 papers in Artificial Intelligence and 3 papers in Signal Processing. Recurrent topics in Pinar Yanardag's work include Generative Adversarial Networks and Image Synthesis (8 papers), Topic Modeling (3 papers) and Image Processing and 3D Reconstruction (2 papers). Pinar Yanardag is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (8 papers), Topic Modeling (3 papers) and Image Processing and 3D Reconstruction (2 papers). Pinar Yanardag collaborates with scholars based in United States, Türkiye and Germany. Pinar Yanardag's co-authors include S. V. N. Vishwanathan, Manuel Cebrián, Iyad Rahwan, Hyokun Yun, Shihao Ji, Federico Tombari, James M. Rehg and Thomas Hofmann and has published in prestigious journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Neural Information Processing Systems and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Pinar Yanardag

14 papers receiving 678 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.

Deep Graph Kernels

2015 543 citations Pinar Yanardag, S. V. N. Vishwanathan profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pinar Yanardag United States	7	540	265	184	106	74	15	710
Stefan Schönauer United States	6	425 0.8×	198 0.7×	112 0.6×	216 2.0×	86 1.2×	9	723
Daokun Zhang Australia	3	343 0.6×	67 0.3×	230 1.3×	98 0.9×	80 1.1×	5	484
Da Zheng United States	14	621 1.1×	416 1.6×	75 0.4×	65 0.6×	171 2.3×	42	929
Mingdong Ou China	7	680 1.3×	278 1.0×	504 2.7×	192 1.8×	197 2.7×	9	1.0k
Deyu Bo China	4	441 0.8×	139 0.5×	150 0.8×	30 0.3×	137 1.9×	5	544
Xiaokai Wei United States	13	553 1.0×	121 0.5×	225 1.2×	79 0.7×	190 2.6×	36	696
Anup Rao United States	10	272 0.5×	84 0.3×	140 0.8×	31 0.3×	69 0.9×	28	413
Yuanfei Dai China	7	279 0.5×	78 0.3×	43 0.2×	73 0.7×	57 0.8×	18	412
Qipeng Guo China	11	907 1.7×	219 0.8×	37 0.2×	61 0.6×	118 1.6×	22	1.1k
Zengfeng Huang China	12	487 0.9×	158 0.6×	101 0.5×	38 0.4×	128 1.7×	44	726

Countries citing papers authored by Pinar Yanardag

Since Specialization

Citations

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

Fields of papers citing papers by Pinar Yanardag

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pinar Yanardag

This figure shows the co-authorship network connecting the top 25 collaborators of Pinar Yanardag. A scholar is included among the top collaborators of Pinar Yanardag 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 Pinar Yanardag. Pinar Yanardag 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:

15 of 15 papers shown

1.

Hofmann, Thomas, et al.. (2025). LoRACLR: Contrastive Adaptation for Customization of Diffusion Models. 13189–13198. 1 indexed citations

2.

Yanardag, Pinar, et al.. (2025). Explaining in Diffusion: Explaining a Classifier with Diffusion Semantics. 14799–14809.

3.

Rehg, James M., et al.. (2024). RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models. 6507–6516. 5 indexed citations

4.

Tombari, Federico, et al.. (2024). CONFORM: Contrast is All You Need For High-Fidelity Text-to-Image Diffusion Models. 9005–9014. 5 indexed citations

5.

Yanardag, Pinar, et al.. (2024). NoiseCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions in Diffusion Models. 24209–24218. 4 indexed citations

6.

Yanardag, Pinar, et al.. (2023). Text and Image Guided 3D Avatar Generation and Manipulation. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4410–4420. 20 indexed citations

7.

Yanardag, Pinar, et al.. (2023). Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 4720–4729. 1 indexed citations

8.

Yanardag, Pinar, et al.. (2022). StyleMC: Multi-Channel Based Fast Text-Guided Image Generation and Manipulation. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 3441–3450. 37 indexed citations

9.

Yanardag, Pinar, et al.. (2022). MIDISpace: Finding Linear Directions in Latent Space for Music Generation. Creativity and Cognition. 420–427. 2 indexed citations

10.

Yanardag, Pinar, et al.. (2022). Rank in Style: A Ranking-based Approach to Find Interpretable Directions. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1. 2293–2297. 1 indexed citations

11.

Yanardag, Pinar, et al.. (2021). LatentCLR: A Contrastive Learning Approach for Unsupervised Discovery of Interpretable Directions. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 14243–14252. 35 indexed citations

12.

Yanardag, Pinar, Manuel Cebrián, & Iyad Rahwan. (2021). Shelley: A Crowd-sourced Collaborative Horror Writer. Creativity and Cognition. 1–8. 11 indexed citations

13.

Ji, Shihao, et al.. (2016). WordRank: Learning Word Embeddings via Robust Ranking. 658–668. 17 indexed citations

14.

Yanardag, Pinar & S. V. N. Vishwanathan. (2015). A structural smoothing framework for Robust graph-comparison. Neural Information Processing Systems. 28. 2134–2142. 28 indexed citations

15.

Yanardag, Pinar & S. V. N. Vishwanathan. (2015). Deep Graph Kernels. 1365–1374. 543 indexed citations breakdown →

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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