no code implementations • 19 Feb 2024 • Miri Varshavsky Hassid, Roy Hirsch, Regev Cohen, Tomer Golany, Daniel Freedman, Ehud Rivlin
The incorporation of Denoising Diffusion Models (DDMs) in the Text-to-Speech (TTS) domain is rising, providing great value in synthesizing high quality speech.
no code implementations • 4 Feb 2024 • Edo Cohen-Karlik, Eyal Rozenberg, Daniel Freedman
Graph generation is a fundamental problem in various domains, including chemistry and social networks.
1 code implementation • 1 Feb 2024 • Liran Ringel, Regev Cohen, Daniel Freedman, Michael Elad, Yaniv Romano
This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification.
no code implementations • 26 Oct 2023 • Roy Hirsch, Regev Cohen, Mathilde Caron, Tomer Golany, Daniel Freedman, Ehud Rivlin
A key element of computer-assisted surgery systems is phase recognition of surgical videos.
1 code implementation • 23 Aug 2023 • Roy Hirsch, Mathilde Caron, Regev Cohen, Amir Livne, Ron Shapiro, Tomer Golany, Roman Goldenberg, Daniel Freedman, Ehud Rivlin
To fully exploit the power of SSL, we create sizable unlabeled endoscopic video datasets for training MSNs.
Ranked #2 on Surgical phase recognition on Cholec80
1 code implementation • 17 May 2023 • Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, Michael Elad
Uncertainty quantification for inverse problems in imaging has drawn much attention lately.
no code implementations • 13 Apr 2023 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer, Sivan Trajtenberg-Mills, Sarika Mishra, Shashi Prabhakar, Ravindra Pratap, Daniel Freedman, Alex M. Bronstein, Ady Arie
We propose a novel, physically-constrained and differentiable approach for the generation of D-dimensional qudit states via spontaneous parametric down-conversion (SPDC) in quantum optics.
no code implementations • 13 Apr 2023 • Eyal Rozenberg, Daniel Freedman
We demonstrate the effectiveness of the technique in the molecular setting of receptor-aware ligand generation.
no code implementations • 28 Nov 2022 • Gilad Kutiel, Regev Cohen, Michael Elad, Daniel Freedman
Our approach is agnostic to the underlying image-to-image network, and only requires triples of the input (degraded), reconstructed and true images for training.
no code implementations • 9 Nov 2022 • Eyal Rozenberg, Daniel Freedman
We propose an algorithm for learning a conditional generative model of a molecule given a target.
no code implementations • 27 Sep 2022 • Ajay Kumar Tanwani, Joelle Barral, Daniel Freedman
We formulate the problem in a visual question answering setting to handle both categorical and descriptive natural language answers.
1 code implementation • 11 Dec 2021 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Joshua Foley-Comer, Sivan Trajtenberg-Mills, Daniel Freedman, Alex M. Bronstein, Ady Arie
In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as the spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.
no code implementations • NeurIPS 2021 • Regev Cohen, Yochai Blau, Daniel Freedman, Ehud Rivlin
In this work, we introduce image denoisers derived as the gradients of smooth scalar-valued deep neural networks, acting as potentials.
no code implementations • 6 Jul 2021 • Xinyi Wei, Hans van Gorp, Lizeth Gonzalez Carabarin, Daniel Freedman, Yonina C. Eldar, Ruud J. G. van Sloun
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements.
no code implementations • 20 Feb 2021 • Eyal Rozenberg, Aviv Karnieli, Ofir Yesharim, Sivan Trajtenberg-Mills, Daniel Freedman, Alex M. Bronstein, Ady Arie
We introduce a systematic approach for designing 3D nonlinear photonic crystals and pump beams for generating desired quantum correlations between structured photon-pairs.
no code implementations • 29 Sep 2020 • Elias Nehme, Boris Ferdman, Lucien E. Weiss, Tal Naor, Daniel Freedman, Tomer Michaeli, Yoav Shechtman
A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity.
no code implementations • ICML 2020 • Tomer Golany, Daniel Freedman, Kira Radinsky
Generating training examples for supervised tasks is a long sought after goal in AI.
no code implementations • 23 Jan 2020 • Daniel Freedman, Yochai Blau, Liran Katzir, Amit Aides, Ilan Shimshoni, Danny Veikherman, Tomer Golany, Ariel Gordon, Greg Corrado, Yossi Matias, Ehud Rivlin
Our coverage algorithm is the first such algorithm to be evaluated in a large-scale way; while our depth estimation technique is the first calibration-free unsupervised method applied to colonoscopies.
no code implementations • 20 Oct 2019 • Micha Feigin, Manuel Zwecker, Daniel Freedman, Brian W. Anthony
Functional muscle imaging is essential for diagnostics of a multitude of musculoskeletal afflictions such as degenerative muscle diseases, muscle injuries, muscle atrophy, and neurological related issues such as spasticity.
no code implementations • 19 Sep 2019 • Eyal Rozenberg, Daniel Freedman, Alex Bronstein
We present such a technique for localization with limited annotation, in which the number of images with bounding boxes can be a small fraction of the total dataset (e. g. less than 1%); all other images only possess a whole image label and no bounding box.
1 code implementation • 21 Jun 2019 • Elias Nehme, Daniel Freedman, Racheli Gordon, Boris Ferdman, Lucien E. Weiss, Onit Alalouf, Reut Orange, Tomer Michaeli, Yoav Shechtman
Localization microscopy is an imaging technique in which the positions of individual nanoscale point emitters (e. g. fluorescent molecules) are determined at high precision from their images.
1 code implementation • 6 Dec 2018 • Alona Golts, Daniel Freedman, Michael Elad
Instead of feeding the network with synthetic data, we solely use real-world outdoor images and tune the network's parameters by directly minimizing the DCP.
Ranked #21 on Image Dehazing on SOTS Outdoor
no code implementations • 30 Sep 2018 • Micha Feigin, Daniel Freedman, Brian W. Anthony
Conclusion: Sound speed inversion on channel data has significant potential, made possible in real time with deep learning technologies.
1 code implementation • 31 May 2018 • Alona Golts, Daniel Freedman, Michael Elad
The success of deep learning has been due, in no small part, to the availability of large annotated datasets.
Ranked #22 on Image Dehazing on SOTS Outdoor
1 code implementation • ICLR 2019 • Or Litany, Daniel Freedman
We present SOSELETO (SOurce SELEction for Target Optimization), a new method for exploiting a source dataset to solve a classification problem on a target dataset.
no code implementations • 4 Dec 2015 • Or Litany, Tal Remez, Daniel Freedman, Lior Shapira, Alex Bronstein, Ran Gal
We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts.
no code implementations • CVPR 2014 • Eyal Krupka, Alon Vinnikov, Ben Klein, Aharon Bar Hillel, Daniel Freedman, Simon Stachniak
The classifier architecture is designed to optimize both classification speed and accuracy when a large training set is available.
no code implementations • 24 Mar 2014 • Daniel Freedman, Eyal Krupka, Yoni Smolin, Ido Leichter, Mirko Schmidt
A major issue with Time of Flight sensors is the presence of multipath interference.