3 papers
cs.CV2022
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…
cs.CV2021
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…
cs.CV2018
Dense xUnit Networks
Idan Kligvasser, Tomer Michaeli
Deep net architectures have constantly evolved over the past few years, leading to significant advancements in a wide array of computer vision tasks. However, besides high accuracy…