What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer vision
- Computer graphics
The scientist’s investigation covers issues in Artificial intelligence, Computer graphics, Computer vision, Rendering and Algorithm.
His Artificial intelligence research integrates issues from Portrait, Machine learning and Pattern recognition.
His Computer graphics study in the realm of Computer graphics connects with subjects such as Scalability.
His Computer vision study integrates concerns from other disciplines, such as Benchmark and Non-photorealistic rendering.
The study incorporates disciplines such as Animation, Viewpoints, WYSIWYG and 3d model in addition to Rendering.
His work on Computational geometry as part of his general Algorithm study is frequently connected to Context, thereby bridging the divide between different branches of science.
His most cited work include:
- PatchMatch: a randomized correspondence algorithm for structural image editing (1885 citations)
- Fast multiresolution image querying (705 citations)
- The generalized patchmatch correspondence algorithm (490 citations)
What are the main themes of his work throughout his whole career to date?
Adam Finkelstein mainly investigates Artificial intelligence, Computer vision, Computer graphics, Rendering and Animation.
His research investigates the connection between Artificial intelligence and topics such as Speech recognition that intersect with issues in Deep learning.
In Computer vision, he works on issues like Non-photorealistic rendering, which are connected to Image-based modeling and rendering.
His work in the fields of Computer graphics and Computer animation overlaps with other areas such as Line drawings and Line.
His research in Computer graphics intersects with topics in Visualization, Human–computer interaction, Multimedia and Data set.
His Rendering research includes elements of 3d model and Software rendering.
He most often published in these fields:
- Artificial intelligence (57.03%)
- Computer vision (40.62%)
- Computer graphics (39.06%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (57.03%)
- Speech recognition (10.94%)
- Deep learning (6.25%)
In recent papers he was focusing on the following fields of study:
Adam Finkelstein spends much of his time researching Artificial intelligence, Speech recognition, Deep learning, Artificial neural network and Noise reduction.
Adam Finkelstein combines subjects such as Metric and Computer vision with his study of Artificial intelligence.
Adam Finkelstein has included themes like Robot and Tile in his Computer vision study.
As a part of the same scientific family, Adam Finkelstein mostly works in the field of Speech recognition, focusing on Face and, on occasion, Representation and Graphics.
His Artificial neural network research incorporates elements of Differentiable function and Pattern recognition.
His work focuses on many connections between Pattern recognition and other disciplines, such as Feature, that overlap with his field of interest in Noise.
Between 2017 and 2021, his most popular works were:
- PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup (152 citations)
- Fftnet: A Real-Time Speaker-Dependent Neural Vocoder (73 citations)
- Text-based editing of talking-head video (68 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer vision
- Computer graphics
His scientific interests lie mostly in Artificial intelligence, Speech recognition, Deep learning, Artificial neural network and Construct.
Adam Finkelstein studies Noise reduction, a branch of Artificial intelligence.
His Speech recognition research incorporates themes from Face and Graphics.
His Deep learning study combines topics in areas such as Convolution and Convolutional neural network.
His Artificial neural network research incorporates themes from Differentiable function, Noise and Pairwise comparison.
His Construct study spans across into areas like Portrait, Range, Function and Style.
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