Search Results for author: Aviv Gabbay

Found 8 papers, 4 papers with code

An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild

1 code implementation NeurIPS 2021 Aviv Gabbay, Niv Cohen, Yedid Hoshen

Unsupervised disentanglement has been shown to be theoretically impossible without inductive biases on the models and the data.

Disentanglement Image Manipulation

Scaling-up Disentanglement for Image Translation

1 code implementation ICCV 2021 Aviv Gabbay, Yedid Hoshen

In this work, we propose OverLORD, a single framework for disentangling labeled and unlabeled attributes as well as synthesizing high-fidelity images, which is composed of two stages; (i) Disentanglement: Learning disentangled representations with latent optimization.

Disentanglement Translation

Learning Disentangled Representations for Image Translation

no code implementations1 Jan 2021 Aviv Gabbay, Yedid Hoshen

Recent approaches for unsupervised image translation are strongly reliant on generative adversarial training and architectural locality constraints.

Disentanglement Translation

Improving Style-Content Disentanglement in Image-to-Image Translation

no code implementations9 Jul 2020 Aviv Gabbay, Yedid Hoshen

Unsupervised image-to-image translation methods have achieved tremendous success in recent years.

Disentanglement Translation +1

Demystifying Inter-Class Disentanglement

2 code implementations ICLR 2020 Aviv Gabbay, Yedid Hoshen

Learning to disentangle the hidden factors of variations within a set of observations is a key task for artificial intelligence.

Clustering Disentanglement +2

Style Generator Inversion for Image Enhancement and Animation

1 code implementation5 Jun 2019 Aviv Gabbay, Yedid Hoshen

We show that style generators outperform other GANs as well as Deep Image Prior as priors for image enhancement tasks.

Image Enhancement Image Manipulation +1

Visual Speech Enhancement

no code implementations23 Nov 2017 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.

Lipreading Speech Enhancement

Seeing Through Noise: Visually Driven Speaker Separation and Enhancement

no code implementations22 Aug 2017 Aviv Gabbay, Ariel Ephrat, Tavi Halperin, Shmuel Peleg

Isolating the voice of a specific person while filtering out other voices or background noises is challenging when video is shot in noisy environments.

Speaker Separation

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