17 citations · 34 across the 4 of their papers we have counts for
7 papers
An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild
Aviv Gabbay, Niv Cohen, Yedid Hoshen
Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data. As an alternative approach, recent methods rely on l…
Scaling-up Disentanglement for Image Translation
Aviv Gabbay, Yedid Hoshen
Image translation methods typically aim to manipulate a set of labeled attributes (given as supervision at training time e.g. domain label) while leaving the unlabeled attributes i…
Improving Style-Content Disentanglement in Image-to-Image Translation
Aviv Gabbay, Yedid Hoshen
Unsupervised image-to-image translation methods have achieved tremendous success in recent years. However, it can be easily observed that their models contain significant entanglem…
Style Generator Inversion for Image Enhancement and Animation
Aviv Gabbay, Yedid Hoshen
One of the main motivations for training high quality image generative models is their potential use as tools for image manipulation. Recently, generative adversarial networks (GAN…
Demystifying Inter-Class Disentanglement
Aviv Gabbay, Yedid Hoshen
Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence. We present a unified formulation for class and cont…
Visual Speech Enhancement
Aviv Gabbay, Asaph Shamir, Shmuel Peleg
When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise. While most existing…
Seeing Through Noise: Visually Driven Speaker Separation and Enhancement
Aviv Gabbay, Ariel Ephrat, Tavi Halperin +1
Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments. We propose audio-visual meth…