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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) proposed a scheme to reconstruct training samples from multilayer perceptron binary classifiers, effectively demonstrating that a large portion of training samples are encoded in the parameters of such networks.
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 samples from neural network binary classifiers, based on theoretical results about the implicit bias of gradient methods.
[Submitted on 21 Nov 2022 ( v1 ), last revised 19 Jun 2023 (this version, v3)] Title:SinFusion: Training Diffusion Models on a Single Image or Video Download a PDF of the paper titled SinFusion: Training Diffusion Models on a Single Image or Video, by Yaniv Nikankin and 1 other authors Download PDF Abstract: Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity.
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