| From word embeddings to document distances M Kusner, Y Sun, N Kolkin, K Weinberger International conference on machine learning, 957-966, 2015 | 2902 | 2015 |
| Style transfer by relaxed optimal transport and self-similarity N Kolkin, J Salavon, G Shakhnarovich Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 305 | 2019 |
| Diode: A dense indoor and outdoor depth dataset I Vasiljevic, N Kolkin, S Zhang, R Luo, H Wang, FZ Dai, AF Daniele, ... arXiv preprint arXiv:1908.00463, 2019 | 209 | 2019 |
| Arf: Artistic radiance fields K Zhang, N Kolkin, S Bi, F Luan, Z Xu, E Shechtman, N Snavely European Conference on Computer Vision, 717-733, 2022 | 174 | 2022 |
| Deformable style transfer SSY Kim, N Kolkin, J Salavon, G Shakhnarovich Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 63 | 2020 |
| Neural neighbor style transfer N Kolkin, M Kucera, S Paris, D Sykora, E Shechtman, G Shakhnarovich arXiv e-prints, arXiv: 2203.13215, 2022 | 15 | 2022 |
| Generative models: What do they know? do they know things? let's find out! X Du, N Kolkin, G Shakhnarovich, A Bhattad arXiv preprint arXiv:2311.17137, 2023 | 12 | 2023 |
| A data perspective on enhanced identity preservation for diffusion personalization X He, Z Cao, N Kolkin, L Yu, K Wan, H Rhodin, R Kalarot arXiv preprint arXiv:2311.04315, 2023 | 9 | 2023 |
| Harnessing the conditioning sensorium for improved image translation C Nederhood, N Kolkin, D Fu, J Salavon Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 5 | 2021 |
| Training deep networks to be spatially sensitive N Kolkin, E Shechtman, G Shakhnarovich Proceedings of the IEEE International Conference on Computer Vision, 5668-5677, 2017 | 5 | 2017 |
| Diverse sampling for self-supervised learning of semantic segmentation M Mostajabi, N Kolkin, G Shakhnarovich arXiv preprint arXiv:1612.01991, 2016 | 4 | 2016 |
| Intrinsic LoRA: A Generalist Approach for Discovering Knowledge in Generative Models X Du, N Kolkin, G Shakhnarovich, A Bhattad Synthetic Data for Computer Vision Workshop@ CVPR 2024, 0 | 3 | |
| Diff-nst: Diffusion interleaving for deformable neural style transfer D Ruta, GC Tarrés, A Gilbert, E Shechtman, N Kolkin, J Collomosse arXiv preprint arXiv:2307.04157, 2023 | 2 | 2023 |
| NeAT: Neural Artistic Tracing for Beautiful Style Transfer D Ruta, A Gilbert, J Collomosse, E Shechtman, N Kolkin arXiv preprint arXiv:2304.05139, 2023 | 2 | 2023 |
| Stochastic covariance compression MJ Kusner, NI Kolkin, S Tyree, KQ Weinberger stat 1050, 4, 2014 | 2 | 2014 |
| TurboEdit: Instant text-based image editing Z Wu, N Kolkin, J Brandt, R Zhang, E Shechtman European Conference on Computer Vision, 365-381, 2025 | 1 | 2025 |
| Image generation using a diffusion model NI Kolkin, E Shechtman US Patent App. 18/169,444, 2024 | 1 | 2024 |
| Personalized Residuals for Concept-Driven Text-to-Image Generation C Ham, M Fisher, J Hays, N Kolkin, Y Liu, R Zhang, T Hinz Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Text-free learning of a natural language interface for pretrained face generators X Du, RA Yeh, N Kolkin, E Shechtman, G Shakhnarovich arXiv preprint arXiv:2209.03953, 2022 | 1 | 2022 |
| Image Data Compression for Covariance and Histogram Descriptors MJ Kusner, NI Kolkin, S Tyree, KQ Weinberger arXiv preprint arXiv:1412.1740, 2014 | 1 | 2014 |
Greg ShakhnarovichProfessor, TTI-ChicagoVerified email at ttic.edu