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Computer Science

D-Index
49
Citations
8727
World Ranking
5936
National Ranking
93

Overview

Raja Giryes is a researcher affiliated with Tel Aviv University in Israel, specializing in the field of Computer Science. Their work encompasses a range of subfields including Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Mechanics, Radiology, Nuclear Medicine and Imaging, and Computer Graphics and Computer-Aided Design.

Their research contributions cover multiple topics within these domains, focusing extensively on Domain Adaptation and Few-Shot Learning, Multimodal Machine Learning Applications, 3D Shape Modeling and Analysis, Computer Graphics and Visualization Techniques, Advanced Vision and Imaging, Advanced Neural Network Applications, and Generative Adversarial Networks and Image Synthesis.

Raja Giryes has published papers in several notable venues, frequently appearing in:

  • arXiv (Cornell University)
  • IEEE Open Journal of Signal Processing
  • ACM Transactions on Graphics
  • Computer Graphics Forum
  • IEEE Signal Processing Magazine

Some recent publications by Raja Giryes include:

  • "Autoencoders", 2020, arXiv (Cornell University)
  • "Baby steps towards few-shot learning with multiple semantics", 2022, Pattern Recognition Letters
  • "Orienting point clouds with dipole propagation", 2021, ACM Transactions on Graphics
  • "SPAGHETTI", 2022, ACM Transactions on Graphics
  • "AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder", 2023, arXiv (Cornell University)

The researcher has collaborated frequently with a number of coauthors including Eli Schwartz, Daniel Cohen-Or, Leonid Karlinsky, Rogério Feris, and Sivan Doveh.

Best Publications

  • MeshCNN: a network with an edge

    Rana Hanocka;Amir Hertz;Noa Fish;Raja Giryes

  • Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures

    Unknown

  • RepMet: Representative-Based Metric Learning for Classification and Few-Shot Object Detection

    Leonid Karlinsky;Joseph Shtok;Sivan Harary;Eli Schwartz

  • Point2Mesh: a self-prior for deformable meshes

    Rana Hanocka;Gal Metzer;Raja Giryes;Daniel Cohen-Or

  • Robust Large Margin Deep Neural Networks

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

  • DeepISP: Toward Learning an End-to-End Image Processing Pipeline

    Eli Schwartz;Raja Giryes;Alex M. Bronstein

  • TEXTure: Text-Guided Texturing of 3D Shapes

    Unknown

  • Deep Neural Networks with Random Gaussian Weights: A Universal Classification Strategy?

    Raja Giryes;Guillermo Sapiro;Alex M. Bronstein

  • Image Restoration by Iterative Denoising and Backward Projections

    Tom Tirer;Raja Giryes

  • Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization

    Daniel Jakubovitz;Raja Giryes

  • Delta-encoder: an effective sample synthesis method for few-shot object recognition

    Eli Schwartz;Leonid Karlinsky;Joseph Shtok;Sivan Harary

  • ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

    Rana Hanocka;Noa Fish;Zhenhua Wang;Raja Giryes

  • Generalization Error in Deep Learning

    Daniel Jakubovitz;Raja Giryes;Miguel R. D. Rodrigues

  • The projected GSURE for automatic parameter tuning in iterative shrinkage methods

    Raja Giryes;Michael Elad;Yonina C. Eldar

  • UNIQ: Uniform Noise Injection for Non-Uniform Quantization of Neural Networks

    Chaim Baskin;Eli Schwartz;Evgenii Zheltonozhskii;Natan Liss

  • Mathematics of Deep Learning

    Rene Vidal;Joan Bruna;Raja Giryes;Stefano Soatto

  • Poisson inverse problems by the Plug-and-Play scheme

    Arie Rond;Raja Giryes;Michael Elad

  • Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask

    Harel Haim;Shay Elmalem;Raja Giryes;Alex M. Bronstein

  • Greedy-like algorithms for the cosparse analysis model

    Raja Giryes;Sangnam Nam;Michael Elad;Rémi Gribonval

  • Delta-encoder: an effective sample synthesis method for few-shot object recognition

    Eli Schwartz;Leonid Karlinsky;Joseph Shtok;Sivan Harary

  • TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set.

    Moran Rubin;Omer Stein;Nir A. Turko;Yoav Nygate

  • Learned Convolutional Sparse Coding

    Hillel Sreter;Raja Giryes

  • Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors

    Gilad Cohen;Guillermo Sapiro;Raja Giryes

  • Margin Preservation of Deep Neural Networks.

    Jure Sokolic;Raja Giryes;Guillermo Sapiro;Miguel R. D. Rodrigues

Frequent Co-Authors

Alexander M. Bronstein
Alexander M. Bronstein Technion – Israel Institute of Technology
Michael Elad
Michael Elad Technion – Israel Institute of Technology
Daniel Cohen-Or
Daniel Cohen-Or Tel Aviv University
Rogerio Feris
Rogerio Feris IBM (United States)
Guillermo Sapiro
Guillermo Sapiro Princeton University
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science
Miguel R. D. Rodrigues
Miguel R. D. Rodrigues University College London
Shai Avidan
Shai Avidan Tel Aviv University
Alfred M. Bruckstein
Alfred M. Bruckstein Technion – Israel Institute of Technology
David Mendlovic
David Mendlovic Tel Aviv University

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