no code implementations • 26 Apr 2024 • Deborah Pereg
In this work, we try to answer a fundamental question: Can supervised learning models generalize well solely by learning from one image or even part of an image?
no code implementations • 11 Nov 2023 • Deborah Pereg
We introduce Back to Basics (BTB), a fast iterative algorithm for noise reduction.
no code implementations • 13 Jun 2023 • Deborah Pereg
In recent years, there have been significant advances in leveraging deep learning methods for noise reduction.
no code implementations • 28 Sep 2022 • Deborah Pereg, Martin Villiger, Brett Bouma, Polina Golland
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset.
no code implementations • 29 Jun 2021 • Deborah Pereg, Israel Cohen, Anthony A. Vassiliou
In sparse coding, we attempt to extract features of input vectors, assuming that the data is inherently structured as a sparse superposition of basic building blocks.
no code implementations • 19 Sep 2013 • Deborah Pereg, Doron Benzvi
In this paper a successful attempt has been made to apply the algorithm to a wider range of signals, such as to process distorted audio signals and destructed images.