Search Results for author: Niv Haim

Found 7 papers, 4 papers with code

Reconstructing Training Data from Trained Neural Networks

no code implementations15 Jun 2022 Niv Haim, Gal Vardi, Gilad Yehudai, Ohad Shamir, Michal Irani

We propose a novel reconstruction scheme that stems from recent theoretical results about the implicit bias in training neural networks with gradient-based methods.

Diverse Video Generation from a Single Video

no code implementations11 May 2022 Niv Haim, Ben Feinstein, Niv Granot, Assaf Shocher, Shai Bagon, Tali Dekel, Michal Irani

GANs are able to perform generation and manipulation tasks, trained on a single video.

Video Generation

From Discrete to Continuous Convolution Layers

1 code implementation19 Jun 2020 Assaf Shocher, Ben Feinstein, Niv Haim, Michal Irani

We propose a generalization of the common Conv-layer, from a discrete layer to a Continuous Convolution (CC) Layer.

Implicit Geometric Regularization for Learning Shapes

4 code implementations ICML 2020 Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, Yaron Lipman

Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks.

Controlling Neural Level Sets

2 code implementations NeurIPS 2019 Matan Atzmon, Niv Haim, Lior Yariv, Ofer Israelov, Haggai Maron, Yaron Lipman

In turn, the sample network can be used to incorporate the level set samples into a loss function of interest.

Surface Reconstruction

Surface Networks via General Covers

1 code implementation ICCV 2019 Niv Haim, Nimrod Segol, Heli Ben-Hamu, Haggai Maron, Yaron Lipman

Specifically, for the use case of learning spherical signals, our representation provides a low distortion alternative to several popular spherical parameterizations used in deep learning.

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