Samuli Laine
About
In The Last Decade
Samuli Laine
58 papers receiving 2.1k citations
Peers
Comparison fields: 5 of 101
- Computer Vision and Pattern Recognition 1.6k
- Computer Graphics and Computer-Aided Design 1.1k
- Computational Mechanics 457
- Signal Processing 252
- Media Technology 163
Countries citing papers authored by Samuli Laine
This map shows the geographic impact of Samuli Laine'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 Samuli Laine with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuli Laine more than expected).
Fields of papers citing papers by Samuli Laine
This network shows the impact of papers produced by Samuli Laine. 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 Samuli Laine. The network helps show where Samuli Laine may publish in the future.
Co-authorship network of co-authors of Samuli Laine
This figure shows the co-authorship network connecting the top 25 collaborators of Samuli Laine. A scholar is included among the top collaborators of Samuli Laine 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 Samuli Laine. Samuli Laine is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | Training Generative Adversarial Networks with Limited Data | 40 |
| 5 | Semi-supervised semantic segmentation needs strong, high-dimensional perturbations | 48 |
| 6 | Consistency regularization and CutMix for semi-supervised semantic segmentation. | 10 |
| 7 | Self-Supervised Deep Image Denoising. | 4 |
| 8 | Feature-Based Metrics for Exploring the Latent Space of Generative Models. | 15 |
| 9 | Noise2Noise: Learning image restoration without clean data | 208 |
| 10 | Facial Performance Capture with Deep Neural Networks. | 5 |
| 11 | 68 | |
| 12 | 17 | |
| 13 | 13 | |
| 14 | 21 | |
| 15 | 96 | |
| 16 | 88 | |
| 17 | 8 | |
| 18 | 9 | |
| 19 | 76 | |
| 20 | 11 |
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.