H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 10,029 69 World Ranking 6905 National Ranking 51

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Computer graphics
  • Computer vision

His main research concerns Artificial intelligence, Computer vision, Image quality, Machine learning and Rendering. His research is interdisciplinary, bridging the disciplines of Bidirectional reflectance distribution function and Artificial intelligence. His study in the field of Noise removal and Noise reduction is also linked to topics like Signal reconstruction, Statistical reasoning and Simple.

His research investigates the link between Image quality and topics such as Normalization that cross with problems in Image resolution. His work in the fields of Machine learning, such as Stability, intersects with other areas such as Discriminator, Metric and Generator. His work deals with themes such as Distributed ray tracing and Computer graphics, which intersect with Rendering.

His most cited work include:

  • Progressive Growing of GANs for Improved Quality, Stability, and Variation (1686 citations)
  • Progressive Growing of GANs for Improved Quality, Stability, and Variation (820 citations)
  • Analyzing and Improving the Image Quality of StyleGAN (503 citations)

What are the main themes of his work throughout his whole career to date?

His primary areas of investigation include Artificial intelligence, Rendering, Computer vision, Computer graphics and Algorithm. His Artificial intelligence study incorporates themes from Machine learning and Pattern recognition. When carried out as part of a general Rendering research project, his work on Global illumination is frequently linked to work in Differentiable function, therefore connecting diverse disciplines of study.

His work on Image restoration and Noise reduction is typically connected to Signal reconstruction, Radiance and Simple as part of general Computer vision study, connecting several disciplines of science. Many of his research projects under Computer graphics are closely connected to Session and Field with Session and Field, tying the diverse disciplines of science together. His work carried out in the field of Image quality brings together such families of science as Normalization, Normalization, Unsupervised learning and Data mining.

He most often published in these fields:

  • Artificial intelligence (67.33%)
  • Rendering (42.57%)
  • Computer vision (43.56%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (67.33%)
  • Pattern recognition (11.88%)
  • Rendering (42.57%)

In recent papers he was focusing on the following fields of study:

Jaakko Lehtinen spends much of his time researching Artificial intelligence, Pattern recognition, Rendering, Artificial neural network and Image. His Artificial intelligence study integrates concerns from other disciplines, such as Machine learning and Computer vision. In general Machine learning, his work in Precision and recall is often linked to Measure and Quality linking many areas of study.

His work in Computer vision tackles topics such as Interpolation which are related to areas like Shader. The study incorporates disciplines such as Pixel and Ray tracing in addition to Rendering. His Artificial neural network study combines topics in areas such as Blind spot and Noisy data.

Between 2018 and 2021, his most popular works were:

  • Analyzing and Improving the Image Quality of StyleGAN (503 citations)
  • Few-Shot Unsupervised Image-to-Image Translation (172 citations)
  • Analyzing and Improving the Image Quality of StyleGAN (93 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer graphics

His primary scientific interests are in Artificial intelligence, Machine learning, Image quality, Benchmark and Normalization. His study focuses on the intersection of Artificial intelligence and fields such as Pattern recognition with connections in the field of Reference data. His work on Overfitting as part of general Machine learning research is frequently linked to Network architecture and Discriminator, thereby connecting diverse disciplines of science.

His Image quality research includes themes of Artificial neural network, Blind spot, Gaussian noise and Impulse noise. His research in Benchmark intersects with topics in Translation and Image translation. He combines subjects such as Image resolution, Normalization, Unsupervised learning and Data mining with his study of Normalization.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Tero Karras;Timo Aila;Samuli Laine;Jaakko Lehtinen.
international conference on learning representations (2018)

1858 Citations

Analyzing and Improving the Image Quality of StyleGAN

Tero Karras;Samuli Laine;Miika Aittala;Janne Hellsten.
computer vision and pattern recognition (2020)

503 Citations

Noise2Noise: Learning image restoration without clean data

Jaakko Lehtinen;Jaakko Lehtinen;Jacob Munkberg;Jon Hasselgren;Samuli Laine.
international conference on machine learning (2018)

397 Citations

Differentiable Monte Carlo ray tracing through edge sampling

Tzu-Mao Li;Miika Aittala;Frédo Durand;Jaakko Lehtinen.
ACM Transactions on Graphics (2018)

217 Citations

Incremental instant radiosity for real-time indirect illumination

Samuli Laine;Hannu Saransaari;Janne Kontkanen;Jaakko Lehtinen.
eurographics symposium on rendering techniques (2007)

173 Citations

Few-Shot Unsupervised Image-to-Image Translation

Ming-Yu Liu;Xun Huang;Arun Mallya;Tero Karras.
international conference on computer vision (2019)

172 Citations

Training Generative Adversarial Networks with Limited Data

Tero Karras;Miika Aittala;Janne Hellsten;Samuli Laine.
neural information processing systems (2020)

168 Citations

Audio-driven facial animation by joint end-to-end learning of pose and emotion

Tero Karras;Timo Aila;Samuli Laine;Antti Herva.
ACM Transactions on Graphics (2017)

157 Citations

Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer

Wenzheng Chen;Huan Ling;Jun Gao;Edward J. Smith.
neural information processing systems (2019)

142 Citations

Matrix radiance transfer

Jaakko Lehtinen;Jan Kautz.
interactive 3d graphics and games (2003)

127 Citations

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