62 citations · 181 across the 11 of their papers we have counts for
7 papers · 1 filter
Reconstructing Training Data From Real World Models Trained with Transfer Learning
Yakir Oz, Gilad Yehudai, Gal Vardi +3
Current methods for reconstructing training data from trained classifiers are restricted to very small models, limited training set sizes, and low-resolution images. Such restricti…
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Gon Buzaglo, Niv Haim, Gilad Yehudai +4
Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently, Haim et al. (2022) propo…
Reconstructing Training Data from Multiclass Neural Networks
Gon Buzaglo, Niv Haim, Gilad Yehudai +2
Reconstructing samples from the training set of trained neural networks is a major privacy concern. Haim et al. (2022) recently showed that it is possible to reconstruct training s…
Reconstructing Training Data from Trained Neural Networks
Niv Haim, Gal Vardi, Gilad Yehudai +2
Understanding to what extent neural networks memorize training data is an intriguing question with practical and theoretical implications. In this paper we show that in some cases…
From Discrete to Continuous Convolution Layers
Assaf Shocher, Ben Feinstein, Niv Haim +1
A basic operation in Convolutional Neural Networks (CNNs) is spatial resizing of feature maps. This is done either by strided convolution (donwscaling) or transposed convolution (u…
Implicit Geometric Regularization for Learning Shapes
Amos Gropp, Lior Yariv, Niv Haim +2
Representing shapes as level sets of neural networks has been recently proved to be useful for different shape analysis and reconstruction tasks. So far, such representations were…
Controlling Neural Level Sets
Matan Atzmon, Niv Haim, Lior Yariv +3
The level sets of neural networks represent fundamental properties such as decision boundaries of classifiers and are used to model non-linear manifold data such as curves and surf…