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.
Few-Shot Unsupervised Image-to-Image Translation
2019329 citationsMing-Yu Liu, Xun Huang et al.Aaltodoc (Aalto University)profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Timo Aila'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 Timo Aila with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Aila more than expected).
This network shows the impact of papers produced by Timo Aila. 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 Timo Aila. The network helps show where Timo Aila may publish in the future.
Co-authorship network of co-authors of Timo Aila
This figure shows the co-authorship network connecting the top 25 collaborators of Timo Aila.
A scholar is included among the top collaborators of Timo Aila 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 Timo Aila. Timo Aila is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Karras, Tero, Miika Aittala, Janne Hellsten, et al.. (2020). Training Generative Adversarial Networks with Limited Data. Neural Information Processing Systems. 33. 12104–12114.40 indexed citations
Lehtinen, Jaakko, Jacob Munkberg, Jon Hasselgren, et al.. (2018). Noise2Noise: Learning image restoration without clean data. Aaltodoc (Aalto University). 4620–4631.208 indexed citations
9.
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Inference. International Conference on Learning Representations.123 indexed citations
10.
Molchanov, Pavlo, Stephen Tyree, Tero Karras, Timo Aila, & Jan Kautz. (2016). Pruning Convolutional Neural Networks for Resource Efficient Transfer Learning.. arXiv (Cornell University).197 indexed citations
11.
Laine, Samuli, et al.. (2016). Facial Performance Capture with Deep Neural Networks.. arXiv (Cornell University).5 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.