no code implementations • 13 Mar 2023 • Aran Carmon, Lior Wolf
In the task of automatic program synthesis, one obtains pairs of matching inputs and outputs and generates a computer program, in a particular domain-specific language (DSL), which given each sample input returns the matching output.
no code implementations • 12 Mar 2023 • Michael Rotman, Lior Wolf
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts.
1 code implementation • 22 Feb 2023 • Erez Sheffi, Michael Rotman, Lior Wolf
However, in order to manipulate a real-world image, one first needs to be able to retrieve its corresponding latent representation in StyleGAN's latent space that is decoded to an image as close as possible to the desired image.
no code implementations • 31 Jan 2023 • Amit Rozner, Barak Battash, Lior Wolf, Ofir Lindenbaum
In this work, we are given unlabeled samples from multiple source domains, and we aim to learn a shared classifier that assigns the examples to various clusters.
1 code implementation • 31 Jan 2023 • Hila Chefer, Yuval Alaluf, Yael Vinker, Lior Wolf, Daniel Cohen-Or
Recent text-to-image generative models have demonstrated an unparalleled ability to generate diverse and creative imagery guided by a target text prompt.
no code implementations • 25 Jan 2023 • Shahar Lutati, Eliya Nachmani, Lior Wolf
Applying a diffusion model Vocoder that was pretrained to model single-speaker voices on the output of a deterministic separation model leads to state-of-the-art separation results.
Ranked #1 on
Speech Separation
on Libri20Mix
no code implementations • 25 Nov 2022 • Tomer Friedlander, Ron Shmelkin, Lior Wolf
The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems.
1 code implementation • 21 Oct 2022 • Oded Hupert, Idan Schwartz, Lior Wolf
We seek to semantically describe a set of images, capturing both the attributes of single images and the variations within the set.
1 code implementation • 2 Oct 2022 • Shahar Lutati, Lior Wolf
We present a dynamic model in which the weights are conditioned on an input sample x and are learned to match those that would be obtained by finetuning a base model on x and its label y.
Ranked #1 on
Image Classification
on Tiny ImageNet Classification
no code implementations • 16 Sep 2022 • Yoni Choukroun, Lior Wolf
Error correction code (ECC) is an integral part of the physical communication layer, ensuring reliable data transfer over noisy channels.
no code implementations • 28 Jul 2022 • Michael Rotman, Amit Dekel, Ran Ilan Ber, Lior Wolf, Yaron Oz
As a result, it successfully propagates initial conditions in continuous time steps by employing the general composition properties of the partial differential operators.
1 code implementation • 22 Jul 2022 • Yoad Tewel, Yoav Shalev, Roy Nadler, Idan Schwartz, Lior Wolf
We introduce a zero-shot video captioning method that employs two frozen networks: the GPT-2 language model and the CLIP image-text matching model.
no code implementations • 18 Jul 2022 • Lior Ben-Moshe, Sagie Benaim, Lior Wolf
We then use a separate set of side images to model the structure of generated images using an autoregressive model trained on the learned patch embeddings of training images.
1 code implementation • 19 Jun 2022 • Tal Shaharabany, Yoad Tewel, Lior Wolf
Moreover, training takes place in a weakly supervised setting, where no bounding boxes are provided.
no code implementations • 5 Jun 2022 • Alon Levkovitch, Eliya Nachmani, Lior Wolf
At the heart of the method lies a sampling process that combines the estimation of the denoising model with a low-pass version of the new speaker's sample.
1 code implementation • 2 Jun 2022 • Hila Chefer, Idan Schwartz, Lior Wolf
It has been observed that visual classification models often rely mostly on the image background, neglecting the foreground, which hurts their robustness to distribution changes.
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
no code implementations • 24 May 2022 • Shahar Lutati, Eliya Nachmani, Lior Wolf
We present an upper bound for the Single Channel Speech Separation task, which is based on an assumption regarding the nature of short segments of speech.
Ranked #2 on
Speech Separation
on Libri10Mix
1 code implementation • 22 May 2022 • Raphael Bensadoun, Shir Gur, Nitsan Blau, Tom Shenkar, Lior Wolf
In this work, we propose a neural IK method that employs the hierarchical structure of the problem to sequentially sample valid joint angles conditioned on the desired position and on the preceding joints along the chain.
no code implementations • 5 May 2022 • Yaron Gurovich, Sagie Benaim, Lior Wolf
This problem is tackled through the lens of disentangled and locally fair representations.
1 code implementation • 11 Apr 2022 • Roni Paiss, Hila Chefer, Lior Wolf
To mitigate it, we present a novel explainability-based approach, which adds a loss term to ensure that CLIP focuses on all relevant semantic parts of the input, in addition to employing the CLIP similarity loss used in previous works.
1 code implementation • 30 Mar 2022 • Yoav Shalev, Lior Wolf
We study the problem of syncing the lip movement in a video with the audio stream.
1 code implementation • 27 Mar 2022 • Yoni Choukroun, Lior Wolf
Error correction code is a major part of the communication physical layer, ensuring the reliable transfer of data over noisy channels.
1 code implementation • 23 Mar 2022 • Tomer Friedlander, Lior Wolf
Canonical Correlation Analysis (CCA) is a method for feature extraction of two views by finding maximally correlated linear projections of them.
no code implementations • CVPR 2022 • Yaniv Benny, Lior Wolf
In this paper, we reveal some of the causes that affect the generation quality of diffusion models, especially when sampling with few iterations, and come up with a simple, yet effective, solution to mitigate them.
1 code implementation • 15 Feb 2022 • Ameen Ali, Thomas Schnake, Oliver Eberle, Grégoire Montavon, Klaus-Robert Müller, Lior Wolf
Transformers have become an important workhorse of machine learning, with numerous applications.
1 code implementation • ICLR 2022 • Michael Rotman, Amit Dekel, Shir Gur, Yaron Oz, Lior Wolf
This way, the obtained representations are naturally disentangled.
1 code implementation • 21 Dec 2021 • Rotem Leibovitz, Jhonathan Osin, Lior Wolf, Guy Gurevitch, Talma Hendler
We obtain a personal signature of a person's learning progress in a self-neuromodulation task, guided by functional MRI (fMRI).
1 code implementation • 10 Dec 2021 • Itzik Malkiel, Gony Rosenman, Lior Wolf, Talma Hendler
We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data.
1 code implementation • 9 Dec 2021 • Shelly Sheynin, Sagie Benaim, Adam Polyak, Lior Wolf
The separation of the attention layer into local and global counterparts allows for a low computational cost in the number of patches, while still supporting data-dependent localization already at the first layer, as opposed to the static positioning in other visual transformers.
no code implementations • 6 Dec 2021 • Jhonathan Osin, Lior Wolf, Guy Gurevitch, Jackob Nimrod Keynan, Tom Fruchtman-Steinbok, Ayelet Or-Borichev, Shira Reznik Balter, Talma Hendler
We present a deep neural network method for learning a personal representation for individuals that are performing a self neuromodulation task, guided by functional MRI (fMRI).
no code implementations • 5 Dec 2021 • Benjamin Klein, Lior Wolf
In this work, a hierarchical model, Graph Query Expansion (GQE), is presented, which is learned in a supervised manner and performs aggregation over an extended neighborhood of the query, thus increasing the information used from the database when computing the query expansion, and using the structure of the nearest neighbors graph.
no code implementations • 5 Dec 2021 • Tal Shaharabany, Lior Wolf
The leading segmentation methods represent the output map as a pixel grid.
no code implementations • 1 Dec 2021 • Tomer Amit, Tal Shaharbany, Eliya Nachmani, Lior Wolf
Since the diffusion model is probabilistic, it is applied multiple times, and the results are merged into a final segmentation map.
Ranked #1 on
Semantic Segmentation
on ISPRS Vaihingen
(mIoU metric)
1 code implementation • CVPR 2022 • Yoad Tewel, Yoav Shalev, Idan Schwartz, Lior Wolf
While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image.
no code implementations • 28 Nov 2021 • Tal Shaharabany, Lior Wolf
In the weakly supervised localization setting, supervision is given as an image-level label.
no code implementations • 25 Nov 2021 • Or Goren, Eliya Nachmani, Lior Wolf
The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony, by the content of each bar, viewed as a chord.
no code implementations • 26 Oct 2021 • Yoni Choukroun, Lior Wolf
Transformers have become methods of choice in many applications thanks to their ability to represent complex interactions between elements.
1 code implementation • 24 Oct 2021 • Hila Chefer, Sagie Benaim, Roni Paiss, Lior Wolf
We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target.
1 code implementation • 21 Oct 2021 • Ameen Ali, Idan Schwartz, Tamir Hazan, Lior Wolf
Traditionally video and text matching is done by learning a shared embedding space and the encoding of one modality is independent of the other.
no code implementations • 10 Oct 2021 • Eliya Nachmani, Robin San Roman, Lior Wolf
Generative diffusion processes are an emerging and effective tool for image and speech generation.
1 code implementation • NeurIPS 2021 • Raphael Bensadoun, Shir Gur, Tomer Galanti, Lior Wolf
Internal learning for single-image generation is a framework, where a generator is trained to produce novel images based on a single image.
no code implementations • 29 Sep 2021 • Shelly Sheynin, Sagie Benaim, Adam Polyak, Lior Wolf
Due to the expensive quadratic cost of the attention mechanism, either a large patch size is used, resulting in coarse-grained global interactions, or alternatively, attention is applied only on a local region of the image at the expense of long-range interactions.
no code implementations • 29 Sep 2021 • Samrudhdhi Bharatkumar Rangrej, Kevin J Liang, Xi Yin, Guan Pang, Theofanis Karaletsos, Lior Wolf, Tal Hassner
Few-shot learning (FSL) methods aim to generalize a model to new unseen classes using only a small number of support examples.
1 code implementation • ICLR 2022 • Tom Shenkar, Lior Wolf
We consider the task of finding out-of-class samples in tabular data, where little can be assumed on the structure of the data.
1 code implementation • EMNLP 2021 • Efrat Blaier, Itzik Malkiel, Lior Wolf
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not.
no code implementations • 12 Sep 2021 • Barak Battash, Lior Wolf, Tamir Hazan
The cross entropy loss is widely used due to its effectiveness and solid theoretical grounding.
no code implementations • 1 Aug 2021 • Ron Shmelkin, Tomer Friedlander, Lior Wolf
A master face is a face image that passes face-based identity-authentication for a large portion of the population.
no code implementations • 26 Jul 2021 • Guy Oren, Lior Wolf
Catastrophic forgetting is one of the major challenges on the road for continual learning systems, which are presented with an on-line stream of tasks.
1 code implementation • 14 Jun 2021 • Eliya Nachmani, Robin San Roman, Lior Wolf
Moreover, we show that using a mixture of Gaussian noise variables in the diffusion process improves the performance over a diffusion process that is based on a single distribution.
no code implementations • 9 Jun 2021 • Itamar Zimerman, Eliya Nachmani, Lior Wolf
In this work, we combine a novel cryptographic variant of a deep error correcting code technique with a modified SAT solver scheme to apply the attack on AES keys.
no code implementations • 1 Jun 2021 • Michael Rotman, Lior Wolf
As we show, these statistics, similar to image statistics, follow a power law.
no code implementations • 30 May 2021 • Noam Gat, Sagie Benaim, Lior Wolf
We consider the task of upscaling a low resolution thumbnail image of a person, to a higher resolution image, which preserves the person's identity and other attributes.
1 code implementation • 9 May 2021 • Shahar Lutati, Lior Wolf
Our experiments demonstrate that our approach is able to design novel antennas and antenna arrays that are compliant with the design requirements, considerably better than the baseline methods.
2 code implementations • ICCV 2021 • Shelly Sheynin, Sagie Benaim, Lior Wolf
We demonstrate the superiority of our method on both the one-shot and few-shot settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as in the setting of defect detection on MVTec.
1 code implementation • 18 Apr 2021 • Shaked Dovrat, Eliya Nachmani, Lior Wolf
Single channel speech separation has experienced great progress in the last few years.
Ranked #1 on
Speech Separation
on Libri15Mix
no code implementations • 6 Apr 2021 • Robin San-Roman, Eliya Nachmani, Lior Wolf
Generative diffusion models have emerged as leading models in speech and image generation.
1 code implementation • 5 Apr 2021 • Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
no code implementations • EACL 2021 • Itzik Malkiel, Lior Wolf
Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task.
1 code implementation • ICCV 2021 • Hila Chefer, Shir Gur, Lior Wolf
Transformers are increasingly dominating multi-modal reasoning tasks, such as visual question answering, achieving state-of-the-art results thanks to their ability to contextualize information using the self-attention and co-attention mechanisms.
1 code implementation • 25 Mar 2021 • Ameen Ali, Tal Shaharabany, Lior Wolf
Radiologist examination of chest CT is an effective way for screening COVID-19 cases.
no code implementations • 22 Mar 2021 • Ameen Ali, Tomer Galanti, Evgeniy Zheltonozhskiy, Chaim Baskin, Lior Wolf
We consider the problem of the extraction of semantic attributes, supervised only with classification labels.
no code implementations • 31 Jan 2021 • Adam Polyak, Lior Wolf, Yossi Adi, Ori Kabeli, Yaniv Taigman
Speech enhancement has seen great improvement in recent years mainly through contributions in denoising, speaker separation, and dereverberation methods that mostly deal with environmental effects on vocal audio.
no code implementations • 23 Jan 2021 • Eliya Nachmani, Lior Wolf
We revisit recent methods that employ graph neural networks for decoding error correcting codes and employ messages that are computed in an autoregressive manner.
no code implementations • ICLR 2021 • Eli Ovits, Lior Wolf
Adiabatic quantum computation is a form of computation that acts by slowly interpolating a quantum system between an easy to prepare initial state and a final state that represents a solution to a given computational problem.
no code implementations • 1 Jan 2021 • Yoav Shalev, Lior Wolf
Conditioned on the source image, the transformed mask is then decoded by a multi-scale generator that renders a realistic image, in which the content of the source frame is animated by the pose in the driving video.
no code implementations • 1 Jan 2021 • Michael Rotman, Lior Wolf
We consider a Multi-Armed Bandit problem in which the rewards are non-stationary and are dependent on past actions and potentially on past contexts.
1 code implementation • CVPR 2021 • Yuval Nirkin, Lior Wolf, Tal Hassner
We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder.
Ranked #5 on
Dichotomous Image Segmentation
on DIS-TE1
Dichotomous Image Segmentation
Real-Time Semantic Segmentation
2 code implementations • CVPR 2021 • Hila Chefer, Shir Gur, Lior Wolf
Self-attention techniques, and specifically Transformers, are dominating the field of text processing and are becoming increasingly popular in computer vision classification tasks.
1 code implementation • 3 Dec 2020 • Shir Gur, Ameen Ali, Lior Wolf
However, as we show, these methods are limited in their ability to identify the support for alternative classifications, an effect we name {\em the saliency bias} hypothesis.
no code implementations • CVPR 2021 • Oran Gafni, Oron Ashual, Lior Wolf
The task of motion transfer between a source dancer and a target person is a special case of the pose transfer problem, in which the target person changes their pose in accordance with the motions of the dancer.
2 code implementations • CVPR 2022 • Yoav Shalev, Lior Wolf
We present a novel approach for image-animation of a source image by a driving video, both depicting the same type of object.
2 code implementations • 4 Nov 2020 • Shlomo E. Chazan, Lior Wolf, Eliya Nachmani, Yossi Adi
The proposed approach is composed of several separation heads optimized together with a speaker classification branch.
no code implementations • NeurIPS 2020 • Niv Pekar, Yaniv Benny, Lior Wolf
Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images.
1 code implementation • CVPR 2021 • Oren Nuriel, Sagie Benaim, Lior Wolf
In the setting of robustness, our method improves on both ImageNet-C and Cifar-100-C for multiple architectures.
no code implementations • 23 Sep 2020 • Maor Ivgi, Yaniv Benny, Avichai Ben-David, Jonathan Berant, Lior Wolf
We empirically show on the COCO-STUFF dataset that our approach improves the quality of both the intermediate layout and the final image.
2 code implementations • CVPR 2021 • Yaniv Benny, Niv Pekar, Lior Wolf
First, it searches for relational patterns in multiple resolutions, which allows it to readily detect visual relations, such as location, in higher resolution, while allowing the lower resolution module to focus on semantic relations, such as shape type.
1 code implementation • 30 Aug 2020 • Michael Michelashvili, Lior Wolf
We present a fast and high-fidelity method for music generation, based on specified f0 and loudness, such that the synthesized audio mimics the timbre and articulation of a target instrument.
no code implementations • 27 Aug 2020 • Yuval Nirkin, Lior Wolf, Yosi Keller, Tal Hassner
Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context (e. g., hair, ears, neck).
no code implementations • 6 Aug 2020 • Adam Polyak, Lior Wolf, Yossi Adi, Yaniv Taigman
We present a wav-to-wav generative model for the task of singing voice conversion from any identity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
1 code implementation • 14 Jul 2020 • Michael Rotman, Lior Wolf
We propose a novel recurrent neural network model, where the hidden state $h_t$ is obtained by permuting the vector elements of the previous hidden state $h_{t-1}$ and adding the output of a learned function $b(x_t)$ of the input $x_t$ at time $t$.
1 code implementation • 2 Jul 2020 • Eugene Kharitonov, Morgane Rivière, Gabriel Synnaeve, Lior Wolf, Pierre-Emmanuel Mazaré, Matthijs Douze, Emmanuel Dupoux
Contrastive Predictive Coding (CPC), based on predicting future segments of speech based on past segments is emerging as a powerful algorithm for representation learning of speech signal.
1 code implementation • EMNLP 2021 • Itzik Malkiel, Lior Wolf
When training neural models, it is common to combine multiple loss terms.
no code implementations • 24 Jun 2020 • Michael Rotman, Rafi Brada, Israel Beniaminy, Sangtae Ahn, Christopher J. Hardy, Lior Wolf
Motion artefacts created by patient motion during an MRI scan occur frequently in practice, often rendering the scans clinically unusable and requiring a re-scan.
3 code implementations • NeurIPS 2020 • Shir Gur, Sagie Benaim, Lior Wolf
We consider the task of generating diverse and novel videos from a single video sample.
no code implementations • CVPR 2020 • Oran Gafni, Lior Wolf
We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene.
1 code implementation • ICLR 2020 • Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
no code implementations • 26 Apr 2020 • Yaniv Benny, Tomer Galanti, Sagie Benaim, Lior Wolf
We present two new metrics for evaluating generative models in the class-conditional image generation setting.
1 code implementation • 5 Apr 2020 • Sagie Benaim, Ron Mokady, Amit Bermano, Daniel Cohen-Or, Lior Wolf
In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.
1 code implementation • NeurIPS 2020 • Etai Littwin, Tomer Galanti, Lior Wolf, Greg Yang
{\em Hypernetworks} are architectures that produce the weights of a task-specific {\em primary network}.
4 code implementations • ICML 2020 • Eliya Nachmani, Yossi Adi, Lior Wolf
We present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously.
Ranked #1 on
Speech Separation
on WSJ0-4mix
1 code implementation • CVPR 2020 • Aviram Bar-Haim, Lior Wolf
We propose to modify the common training protocols of optical flow, leading to sizable accuracy improvements without adding to the computational complexity of the training process.
Ranked #7 on
Optical Flow Estimation
on Sintel-final
no code implementations • 23 Feb 2020 • Tomer Galanti, Ofir Nabati, Lior Wolf
In the multivariate case, where one can ensure that the complexities of the cause and effect are balanced, we propose a new adversarial training method that mimics the disentangled structure of the causal model.
1 code implementation • NeurIPS 2020 • Tomer Galanti, Lior Wolf
Besides, we show that for a structured target function, the overall number of trainable parameters in a hypernetwork is smaller by orders of magnitude than the number of trainable parameters of a standard neural network and an embedding method.
1 code implementation • 1 Feb 2020 • Eliya Nachmani, Lior Wolf
In this work, we demonstrate that the replacement of the underlying networks with hypernetworks leads to a boost in performance, obtaining state of the art results in various benchmarks.
no code implementations • 28 Jan 2020 • Etai Littwin, Tomer Galanti, Lior Wolf
We derive finite width and depth corrections for the Neural Tangent Kernel (NTK) of ResNets and DenseNets.
no code implementations • 15 Jan 2020 • Irad Peleg, Lior Wolf
We present Generative Adversarial Networks (GANs), in which the symmetric property of the generated images is controlled.
no code implementations • 15 Jan 2020 • Lior Wolf, Tomer Galanti, Tamir Hazan
We regard explanations as a blending of the input sample and the model's output and offer a few definitions that capture various desired properties of the function that generates these explanations.
no code implementations • 14 Jan 2020 • Omri Lifshitz, Lior Wolf
We study the problem of unsupervised domain adaption in the universal scenario, in which only some of the classes are shared between the source and target domains.
no code implementations • ICML Workshop Deep_Phenomen 2019 • Etai Littwin, Lior Wolf
The Hessian of neural networks can be decomposed into a sum of two matrices: (i) the positive semidefinite generalized Gauss-Newton matrix G, and (ii) the matrix H containing negative eigenvalues.
1 code implementation • CVPR 2019 • Shir Gur, Lior Wolf
We evaluate our method on data derived from five common datasets for depth estimation and lightfield images, and present results that are on par with supervised methods on KITTI and Make3D datasets and outperform unsupervised learning approaches.
Ranked #32 on
Monocular Depth Estimation
on KITTI Eigen split
no code implementations • ICLR 2019 • Lior Wolf, Sagie Benaim, Tomer Galanti
Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x)=1.
1 code implementation • ICLR 2019 • Ori Press, Tomer Galanti, Sagie Benaim, Lior Wolf
Thus, in the above example, we can create, for every person without glasses a version with the glasses observed in any face image.
no code implementations • 14 Jan 2020 • Shir Gur, Lior Wolf, Lior Golgher, Pablo Blinder
Recently developed methods for rapid continuous volumetric two-photon microscopy facilitate the observation of neuronal activity in hundreds of individual neurons and changes in blood flow in adjacent blood vessels across a large volume of living brain at unprecedented spatio-temporal resolution.
no code implementations • ECCV 2020 • Yaniv Benny, Lior Wolf
We present a method for simultaneously learning, in an unsupervised manner, (i) a conditional image generator, (ii) foreground extraction and segmentation, (iii) clustering into a two-level class hierarchy, and (iv) object removal and background completion, all done without any use of annotation.
no code implementations • 30 Dec 2019 • Yoav Chai, Raja Giryes, Lior Wolf
We treat the problem of color enhancement as an image translation task, which we tackle using both supervised and unsupervised learning.
no code implementations • 19 Dec 2019 • Eyal Shulman, Lior Wolf
We build the trees by applying learned regression functions to obtain the decision rules as well as the values at the leaf nodes.
2 code implementations • ICLR 2020 • Shir Gur, Tal Shaharabany, Lior Wolf
We present an image segmentation method that iteratively evolves a polygon.
no code implementations • 26 Nov 2019 • Itzik Malkiel, Michael Mrejen, Lior Wolf, Haim Suchowski
Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry.
no code implementations • 22 Nov 2019 • Barak Itkin, Lior Wolf, Nachum Dershowitz
One method relies on the shape of the fracture outline of a sherd; the other is based on decorative features.
no code implementations • ICCV 2019 • Oran Gafni, Lior Wolf, Yaniv Taigman
We propose a method for face de-identification that enables fully automatic video modification at high frame rates.
no code implementations • 8 Nov 2019 • Michael Rotman, Lior Wolf
A two-stage network is used, which first generates a chain of circuit components and then predicts their parameters.
no code implementations • 8 Nov 2019 • Eliya Nachmani, Lior Wolf
Hypernetworks were recently shown to improve the performance of message passing algorithms for decoding error correcting codes.
1 code implementation • 5 Nov 2019 • Itzik Malkiel, Lior Wolf
In this work, we present a method that leverages BERT's fine-tuning phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training.
1 code implementation • 16 Oct 2019 • Barak Battash, Lior Wolf
The current leading computer vision models are typically feed forward neural models, in which the output of one computational block is passed to the next one sequentially.
no code implementations • 25 Sep 2019 • Etai Littwin, Lior Wolf
A critical part of the training process of neural networks takes place in the very first gradient steps post initialization.
no code implementations • 25 Sep 2019 • Tomer Galanti, Ofir Nabati, Lior Wolf
Comparing the reconstruction errors of the two autoencoders, one for each variable, is shown to perform well on the accepted benchmarks of the field.
no code implementations • 17 Sep 2019 • Taivanbat Badamdorj, Adiel Ben-Shalom, Nachum Dershowitz, Lior Wolf
The indexing and searching of historical documents have garnered attention in recent years due to massive digitization efforts of important collections worldwide.
2 code implementations • ICCV 2019 • Oron Ashual, Lior Wolf
We introduce a method for the generation of images from an input scene graph.
Ranked #4 on
Layout-to-Image Generation
on COCO-Stuff 64x64
1 code implementation • NeurIPS 2019 • Eliya Nachmani, Lior Wolf
Neural decoders were shown to outperform classical message passing techniques for short BCH codes.
1 code implementation • ICCV 2019 • Tomer Cohen, Lior Wolf
We study the problem of mapping between a domain $A$, in which there is a single training sample and a domain $B$, for which we have a richer training set.
1 code implementation • ICCV 2019 • Sagie Benaim, Michael Khaitov, Tomer Galanti, Lior Wolf
We present a method for recovering the shared content between two visual domains as well as the content that is unique to each domain.
1 code implementation • ICCV 2019 • Gidi Littwin, Lior Wolf
We present a new method for 3D shape reconstruction from a single image, in which a deep neural network directly maps an image to a vector of network weights.
1 code implementation • ICCV 2019 • Shir Gur, Lior Wolf, Lior Golgher, Pablo Blinder
We present a novel deep learning method for unsupervised segmentation of blood vessels.
1 code implementation • 15 Jun 2019 • Ron Mokady, Sagie Benaim, Lior Wolf, Amit Bermano
We consider the problem of translating, in an unsupervised manner, between two domains where one contains some additional information compared to the other.
no code implementations • 2 May 2019 • Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
no code implementations • ICLR 2019 • Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman
We present a method for translating music across musical instruments and styles.
no code implementations • 18 Apr 2019 • Adam Polyak, Lior Wolf, Yaniv Taigman
We present a fully convolutional wav-to-wav network for converting between speakers' voices, without relying on text.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
no code implementations • ICLR 2020 • Oran Gafni, Lior Wolf, Yaniv Taigman
The second network maps the current pose, the new pose, and a given background, to an output frame.
1 code implementation • 16 Apr 2019 • Michael Michelashvili, Lior Wolf
The method is completely unsupervised and only trains on the specific audio clip that is being denoised.
no code implementations • 13 Apr 2019 • Eliya Nachmani, Lior Wolf
The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another.
1 code implementation • 1 Apr 2019 • Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, Seong-Whan Lee
As Deep Neural Networks (DNNs) have demonstrated superhuman performance in a variety of fields, there is an increasing interest in understanding the complex internal mechanisms of DNNs.
no code implementations • 6 Feb 2019 • Eliya Nachmani, Lior Wolf
We present a TTS neural network that is able to produce speech in multiple languages.
1 code implementation • 14 Dec 2018 • Michael Michelashvili, Sagie Benaim, Lior Wolf
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music.
no code implementations • NeurIPS 2018 • Etai Littwin, Lior Wolf
Normalization techniques play an important role in supporting efficient and often more effective training of deep neural networks.
1 code implementation • NeurIPS 2018 • Amit Zohar, Lior Wolf
We consider the problem of generating automatic code given sample input-output pairs.
no code implementations • 29 Jul 2018 • Doron Sobol, Lior Wolf, Yaniv Taigman
For example, given a video frame in the target game, we map it to an analogous state in the source game and then attempt to play using a trained policy learned for the source game.
no code implementations • 23 Jul 2018 • Tomer Galanti, Sagie Benaim, Lior Wolf
The recent empirical success of unsupervised cross-domain mapping algorithms, between two domains that share common characteristics, is not well-supported by theoretical justifications.
2 code implementations • NeurIPS 2018 • Sagie Benaim, Lior Wolf
Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B.
1 code implementation • ECCV 2018 • Yedid Hoshen, Lior Wolf
NAM relies on a pre-trained generative model of the target domain, and aligns each source image with an image synthesized from the target domain, while jointly optimizing the domain mapping function.
no code implementations • 28 May 2018 • Noam Mor, Lior Wolf
Having a reliable accuracy score is crucial for real world applications of OCR, since such systems are judged by the number of false readings.
4 code implementations • 21 May 2018 • Noam Mor, Lior Wolf, Adam Polyak, Yaniv Taigman
We present a method for translating music across musical instruments, genres, and styles.
no code implementations • CVPR 2018 • Yedid Hoshen, Lior Wolf
Linking between two data sources is a basic building block in numerous computer vision problems.
no code implementations • 26 Mar 2018 • Yoav Kaempfer, Lior Wolf
While there are optimal TSP solvers, as well as recent learning-based approaches, the generalization of the TSP to the Multiple Traveling Salesmen Problem is much less studied.
no code implementations • ICML 2018 • Eliya Nachmani, Adam Polyak, Yaniv Taigman, Lior Wolf
Learning-based Text To Speech systems have the potential to generalize from one speaker to the next and thus require a relatively short sample of any new voice.
4 code implementations • EMNLP 2018 • Yedid Hoshen, Lior Wolf
We present a novel method that first aligns the second moment of the word distributions of the two languages and then iteratively refines the alignment.
no code implementations • ICLR 2018 • Yedid Hoshen, Lior Wolf
We further show that the cross-domain mapping task can be broken into two parts: domain alignment and learning the mapping function.
1 code implementation • ECCV 2018 • Sagie Benaim, Tomer Galanti, Lior Wolf
While in supervised learning, the validation error is an unbiased estimator of the generalization (test) error and complexity-based generalization bounds are abundant, no such bounds exist for learning a mapping in an unsupervised way.
2 code implementations • 27 Nov 2017 • Eli David, Nathan S. Netanyahu, Lior Wolf
We present an end-to-end learning method for chess, relying on deep neural networks.
no code implementations • CVPR 2019 • Benjamin Klein, Lior Wolf
To our knowledge, this is the first work to introduce a dictionary-based representation that is inspired by Product Quantization and which is learned end-to-end, and thus benefits from the supervised signal.
1 code implementation • CVPR 2018 • Naama Hadad, Lior Wolf, Moni Shahar
First, the part of the data that is correlated with the labels is extracted by training a classifier.
no code implementations • ICLR 2018 • Tomer Galanti, Lior Wolf, Sagie Benaim
We discuss the feasibility of the following learning problem: given unmatched samples from two domains and nothing else, learn a mapping between the two, which preserves semantics.
1 code implementation • ICML 2017 • Dor Levy, Lior Wolf
We propose a new neural network architecture and use it for the task of statement-by-statement alignment of source code and its compiled object code.
1 code implementation • ICLR 2018 • Yaniv Taigman, Lior Wolf, Adam Polyak, Eliya Nachmani
We present a new neural text to speech (TTS) method that is able to transform text to speech in voices that are sampled in the wild.
no code implementations • 6 Jun 2017 • Amir Bar, Lior Wolf, Orna Bergman Amitai, Eyal Toledano, Eldad Elnekave
Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.
3 code implementations • 5 Jun 2017 • Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf
Generative Adversarial Networks (GANs) have shown great promise recently in image generation.
1 code implementation • NeurIPS 2017 • Sagie Benaim, Lior Wolf
In this work, we present a method of learning $G_{AB}$ without learning $G_{BA}$.
Ranked #7 on
Facial Expression Translation
on CelebA
no code implementations • ICCV 2017 • Lior Wolf, Yaniv Taigman, Adam Polyak
We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters.
no code implementations • 5 Mar 2017 • Tomer Galanti, Lior Wolf
We consider the complementary problem in which the unlabeled samples are given post mapping, i. e., we are given the outputs of the mapping of unknown samples from the shifted domain.
no code implementations • 5 Feb 2017 • Nadav Israel, Lior Wolf, Ran Barzilay, Gal Shoval
Automatic recognition of facial gestures is becoming increasingly important as real world AI agents become a reality.
1 code implementation • CVPR 2017 • Amit Shaked, Lior Wolf
We propose a new highway network architecture for computing the matching cost at each possible disparity, based on multilevel weighted residual shortcuts, trained with a hybrid loss that supports multilevel comparison of image patches.
1 code implementation • ICCV 2017 • Dotan Kaufman, Gil Levi, Tal Hassner, Lior Wolf
A test video is processed by forming correspondences between its clips and the clips of reference videos with known semantics, following which, reference semantics can be transferred to the test video.
Ranked #24 on
Video Retrieval
on MSR-VTT
1 code implementation • CVPR 2017 • Shay Zweig, Lior Wolf
The current state-of-the-art method for interpolation, EpicFlow, is a local average method based on an edge aware geodesic distance.
1 code implementation • CVPR 2017 • Tal Schuster, Lior Wolf, David Gadot
This type of training produces a network that displays multiple strategies depending on the input and leads to state of the art results on the KITTI 2012 and KITTI 2015 optical flow benchmarks.
no code implementations • 8 Nov 2016 • Etai Littwin, Lior Wolf
Deep Residual Networks present a premium in performance in comparison to conventional networks of the same depth and are trainable at extreme depths.
6 code implementations • 7 Nov 2016 • Yaniv Taigman, Adam Polyak, Lior Wolf
We study the problem of transferring a sample in one domain to an analog sample in another domain.
no code implementations • 27 Sep 2016 • Orna Almogi, Lena Dankin, Nachum Dershowitz, Lior Wolf
We describe the course of a hackathon dedicated to the development of linguistic tools for Tibetan Buddhist studies.
1 code implementation • CVPR 2017 • Aviv Eisenschtat, Lior Wolf
We show a direct link between the correlation-based loss and Euclidean loss, enabling the use of Euclidean loss for correlation maximization.
Ranked #12 on
Image Retrieval
on Flickr30K 1K test
10 code implementations • EACL 2017 • Ofir Press, Lior Wolf
We study the topmost weight matrix of neural network language models.
no code implementations • CVPR 2016 • Arik Poznanski, Lior Wolf
Given an image of a handwritten word, a CNN is employed to estimate its n-gram frequency profile, which is the set of n-grams contained in the word.
no code implementations • 12 Dec 2015 • Guy Lev, Gil Sadeh, Benjamin Klein, Lior Wolf
Recurrent Neural Networks (RNNs) have had considerable success in classifying and predicting sequences.
no code implementations • CVPR 2016 • David Gadot, Lior Wolf
We propose a new pipeline for optical flow computation, based on Deep Learning techniques.
no code implementations • ICCV 2015 • Ofir Levy, Lior Wolf
The task of counting the number of repetitions of approximately the same action in an input video sequence is addressed.
no code implementations • CVPR 2016 • Etai Littwin, Lior Wolf
Deep learning techniques are renowned for supporting effective transfer learning.
no code implementations • CVPR 2015 • Benjamin Klein, Guy Lev, Gil Sadeh, Lior Wolf
In this work, we are using the Fisher Vector as a sentence representation by pooling the word2vec embedding of each word in the sentence.
Ranked #12 on
Video Retrieval
on YouCook2
no code implementations • CVPR 2015 • Benjamin Klein, Lior Wolf, Yehuda Afek
In contrast, the dynamic convolutional layer uses filters that will vary from input to input during testing.
no code implementations • 26 Nov 2014 • Benjamin Klein, Guy Lev, Gil Sadeh, Lior Wolf
The second Mixture Model presented is a Hybrid Gaussian-Laplacian Mixture Model (HGLMM) which is based on a weighted geometric mean of the Gaussian and Laplacian distribution.
2 code implementations • Conference on Computer Vision and Pattern Recognition (CVPR) 2014 • Yaniv Taigman, Ming Yang, Marc’ Aurelio Ranzato, Lior Wolf
In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify.
Ranked #1 on
3D Face Modelling
on LFW
no code implementations • CVPR 2015 • Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
Scaling machine learning methods to very large datasets has attracted considerable attention in recent years, thanks to easy access to ubiquitous sensing and data from the web.
no code implementations • CVPR 2014 • Itai Ben-Shalom, Noga Levy, Lior Wolf, Nachum Dershowitz, Adiel Ben-Shalom, Roni Shweka, Yaacov Choueka, Tamir Hazan, Yaniv Bar
The utility of the tool is demonstrated within the context of visual search of documents from the Cairo Genizah and for retrieval of paintings by the same artist and in the same style.
no code implementations • CVPR 2013 • Lior Wolf, Noga Levy
Face recognition in unconstrained videos requires specialized tools beyond those developed for still images: the fact that the confounding factors change state during the video sequence presents a unique challenge, but also an opportunity to eliminate spurious similarities.
no code implementations • 4 Aug 2011 • Yaniv Taigman, Lior Wolf
We employ the face recognition technology developed in house at face. com to a well accepted benchmark and show that without any tuning we are able to considerably surpass state of the art results.