19 citations · 48 across the 9 of their papers we have counts for
9 papers · 1 filter
BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning
Idan Kligvasser, Tamar Rott Shaham, Noa Alkobi +1
Training a generative model on a single image has drawn significant attention in recent years. Single image generative methods are designed to learn the internal patch distribution…
Sparsity Aware Normalization for GANs
Idan Kligvasser, Tomer Michaeli
Generative adversarial networks (GANs) are known to benefit from regularization or normalization of their critic (discriminator) network during training. In this paper, we analyze…
GAN "Steerability" without optimization
Nurit Spingarn-Eliezer, Ron Banner, Tomer Michaeli
Recent research has shown remarkable success in revealing "steering" directions in the latent spaces of pre-trained GANs. These directions correspond to semantically meaningful ima…
Spatially-Adaptive Pixelwise Networks for Fast Image Translation
Tamar Rott Shaham, Michael Gharbi, Richard Zhang +2
We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation. We design the generator to be an extremely lightweight function o…
Explorable Super Resolution
Yuval Bahat, Tomer Michaeli
Single image super resolution (SR) has seen major performance leaps in recent years. However, existing methods do not allow exploring the infinitely many plausible reconstructions…
SinGAN: Learning a Generative Model from a Single Natural Image
Tamar Rott Shaham, Tali Dekel, Tomer Michaeli
We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within…