Daniel Zoran

3.7k citations
18 papers · 1.5k · 1 hit paper · h-index 11

Impact in

    • Advanced Image Fusion Techniques
    • Image Processing Techniques and Applications
    • Image and Signal Denoising Methods
    • Advanced Image Processing Techniques
    • Advanced Vision and Imaging
    • Image Enhancement Techniques
    • Generative Adversarial Networks and Image Synthesis

Papers in

    • Image and Signal Denoising Methods 4
    • Generative Adversarial Networks and Image Synthesis 4
    • Advanced Vision and Imaging 4
    • Human Pose and Action Recognition 3
    • Advanced Image and Video Retrieval Techniques 3
    • Multimodal Machine Learning Applications 2
    • Domain Adaptation and Few-Shot Learning 4
    • Adversarial Robustness in Machine Learning 2
Journals
The Astrophysical Journal (1 paper)arXiv (Cornell University) (2 papers)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)Neural Information Processing Systems (5 papers)International Conference on Machine Learning (2 papers)

In The Last Decade

Daniel Zoran

17 papers receiving 1.5k citations

Daniel Zoran's Hit Papers

From learning models of natural image patches to whole image restoration 2011 · 961 citations
9610+5+10Years since publication250500750

Peers

Daniel Zoran
Comparison fields: 5 of 80
  • Media Technology 748
  • Computer Vision and Pattern Recognition 1.3k
  • Computer Graphics and Computer-Aided Design 44
  • Computational Mechanics 236
  • Artificial Intelligence 144
Replace Priyam Chatterjee with:
Priyam Chatterjee United States
Catalina Sbert Spain
Marius Lysaker Norway
Matan Protter Israel
Christian J. Schuler Germany
M.R. Banham United States
Moon Gi Kang South Korea
Yuli You United States
Hongqing Zhu China
Tai-Xiang Jiang China
Daniel Zoran relative to Priyam Chatterjee United States Priyam Chatterjee's profile →
Citations per field
00.5×3.3×
Priyam Chatterjee · 1×
Citations per year

Countries citing papers authored by Daniel Zoran

Since Specialization
Citations

This map shows the geographic impact of Daniel Zoran'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 Daniel Zoran with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Zoran more than expected).

Fields of papers citing papers by Daniel Zoran

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Zoran. 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 Daniel Zoran. The network helps show where Daniel Zoran may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Zoran, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Zoran Line = papers co-authored together Daniel Zoran links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1
From learning models of natural image patches to whole image restoration
Hit paper breakdown →
2011961
2 2009188
3 201595
4
Visual Interaction Networks: Learning a Physics Simulator from Video
201774
5
Natural Images, Gaussian Mixtures and Dead Leaves
201258
6 201436
7
LaVAN: Localized and Visible Adversarial Noise
201823
8
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
202119
9
Learning the Local Statistics of Optical Flow
201315
10
Multi-Object Representation Learning with Iterative Variational Inference
201913
11
Shape and Illumination from Shading using the Generic Viewpoint Assumption
201411
12 20217
13
Learned Deformation Stability in Convolutional Neural Networks.
20186
14
The 'tree-dependent components' of natural scenes are edge filters
20095
15 20245
16
Variational Memory Addressing in Generative Models
20174
17 20241
18
S3TA: A Soft, Spatial, Sequential, Top-Down Attention Model
20181

About Daniel Zoran

Daniel Zoran is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Media Technology and Computer Graphics and Computer-Aided Design, having authored 18 papers that have together received 1.5k indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (4 papers), Image and Signal Denoising Methods (4 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Vision and Imaging (4 papers), Human Pose and Action Recognition (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Multimodal Machine Learning Applications (2 papers) and Adversarial Robustness in Machine Learning (2 papers). The work is most often cited by research in Media Technology (748 citations), Computer Vision and Pattern Recognition (1.3k citations), Computer Graphics and Computer-Aided Design (44 citations), Computational Mechanics (236 citations) and Artificial Intelligence (144 citations). Daniel Zoran has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Yair Weiss, Dilip Krishnan, William T. Freeman, Phillip Isola, Razvan Pascanu, Nicholas Watters, Peter Battaglia, Théophane Weber, Andrea Tacchetti and Yoav Goldberg. Their work appears in journals such as The Astrophysical Journal, arXiv (Cornell University), 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Neural Information Processing Systems and International Conference on Machine Learning.

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.

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