62 citations · 181 across the 11 of their papers we have counts for
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cs.CV2022★ 20 cited
SinFusion: Training Diffusion Models on a Single Image or Video
Yaniv Nikankin, Niv Haim, Michal Irani
Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity. However, they are usually trained on very large datasets and…
cs.LG2022★ 29 cited
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…
cs.CV2022★ 4 cited
Diverse Video Generation from a Single Video
Niv Haim, Ben Feinstein, Niv Granot +4
GANs are able to perform generation and manipulation tasks, trained on a single video. However, these single video GANs require unreasonable amount of time to train on a single vid…