19 citations · 24 across the 2 of their papers we have counts for
8 papers
Learning an optimal PSF-pair for ultra-dense 3D localization microscopy
Elias Nehme, Boris Ferdman, Lucien E. Weiss +4
A long-standing challenge in multiple-particle-tracking is the accurate and precise 3D localization of individual particles at close proximity. One established approach for snapsho…
Unique Properties of Flat Minima in Deep Networks
Rotem Mulayoff, Tomer Michaeli
It is well known that (stochastic) gradient descent has an implicit bias towards flat minima. In deep neural network training, this mechanism serves to screen out minima. However,…
Explorable Super Resolution
Yuval Bahat, Tomer Michaeli
Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions…
DeepSTORM3D: dense three dimensional localization microscopy and point spread function design by deep learning
Elias Nehme, Daniel Freedman, Racheli Gordon +6
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 the…
SinGAN: Learning a Generative Model from a Single Natural Image
Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within…
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Yochai Blau, Tomer Michaeli
Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (…