Search Results for author: Gal Chechik

Found 45 papers, 22 papers with code

On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning

no code implementations13 Oct 2021 Guy Tennenholtz, Assaf Hallak, Gal Dalal, Shie Mannor, Gal Chechik, Uri Shalit

We analyze the limitations of learning from such data with and without external reward, and propose an adjustment of standard imitation learning algorithms to fit this setup.

Imitation Learning Recommendation Systems

Object-Region Video Transformers

no code implementations13 Oct 2021 Roei Herzig, Elad Ben-Avraham, Karttikeya Mangalam, Amir Bar, Gal Chechik, Anna Rohrbach, Trevor Darrell, Amir Globerson

In this work, we present Object-Region Video Transformers (ORViT), an \emph{object-centric} approach that extends video transformer layers with a block that directly incorporates object representations.

Action Detection Action Recognition +1

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

2 code implementations2 Aug 2021 Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or

Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?

Domain Adaptation Image Manipulation

Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction

no code implementations4 Jul 2021 Assaf Hallak, Gal Dalal, Steven Dalton, Iuri Frosio, Shie Mannor, Gal Chechik

We introduce Batch-BFS: a GPU breadth-first search that advances all nodes in each depth of the tree simultaneously.

Atari Games

Personalized Federated Learning with Gaussian Processes

1 code implementation29 Jun 2021 Idan Achituve, Aviv Shamsian, Aviv Navon, Gal Chechik, Ethan Fetaya

A key challenge in this setting is to learn effectively across clients even though each client has unique data that is often limited in size.

 Ranked #1 on Personalized Federated Learning on CIFAR-10 (ACC@1-100Clients metric)

Gaussian Processes Personalized Federated Learning

Distributional Robustness Loss for Long-tail Learning

no code implementations ICCV 2021 Dvir Samuel, Gal Chechik

The new robustness loss can be combined with various classifier balancing techniques and can be applied to representations at several layers of the deep model.

Long-tail Learning

Personalized Federated Learning using Hypernetworks

1 code implementation8 Mar 2021 Aviv Shamsian, Aviv Navon, Ethan Fetaya, Gal Chechik

In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client.

Personalized Federated Learning

ACAV100M: Automatic Curation of Large-Scale Datasets for Audio-Visual Video Representation Learning

no code implementations ICCV 2021 Sangho Lee, Jiwan Chung, Youngjae Yu, Gunhee Kim, Thomas Breuel, Gal Chechik, Yale Song

We demonstrate that our approach finds videos with high audio-visual correspondence and show that self-supervised models trained on our data achieve competitive performances compared to models trained on existing manually curated datasets.

Representation Learning

Teacher-Student Consistency For Multi-Source Domain Adaptation

1 code implementation20 Oct 2020 Ohad Amosy, Gal Chechik

Then, we train a student network using the pseudo labels and regularized the teacher to fit the student predictions.

Domain Adaptation Object Recognition +1

From Local Structures to Size Generalization in Graph Neural Networks

no code implementations17 Oct 2020 Gilad Yehudai, Ethan Fetaya, Eli Meirom, Gal Chechik, Haggai Maron

In this paper, we identify an important type of data where generalization from small to large graphs is challenging: graph distributions for which the local structure depends on the graph size.

Combinatorial Optimization Domain Adaptation +2

Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks

no code implementations11 Oct 2020 Eli A. Meirom, Haggai Maron, Shie Mannor, Gal Chechik

We consider the problem of controlling a partially-observed dynamic process on a graph by a limited number of interventions.

Learning the Pareto Front with Hypernetworks

1 code implementation ICLR 2021 Aviv Navon, Aviv Shamsian, Gal Chechik, Ethan Fetaya

Here, we tackle the problem of learning the entire Pareto front, with the capability of selecting a desired operating point on the front after training.

Fairness Multiobjective Optimization +3

ZEST: Zero-shot Learning from Text Descriptions using Textual Similarity and Visual Summarization

1 code implementation Findings of the Association for Computational Linguistics 2020 Tzuf Paz-Argaman, Yuval Atzmon, Gal Chechik, Reut Tsarfaty

Specifically, given birds' images with free-text descriptions of their species, we learn to classify images of previously-unseen species based on specie descriptions.

Zero-Shot Learning

Learning Object Detection from Captions via Textual Scene Attributes

no code implementations30 Sep 2020 Achiya Jerbi, Roei Herzig, Jonathan Berant, Gal Chechik, Amir Globerson

In this work, we argue that captions contain much richer information about the image, including attributes of objects and their relations.

Image Captioning Object Detection

Compositional Video Synthesis with Action Graphs

1 code implementation27 Jun 2020 Amir Bar, Roei Herzig, Xiaolong Wang, Anna Rohrbach, Gal Chechik, Trevor Darrell, Amir Globerson

Our generative model for this task (AG2Vid) disentangles motion and appearance features, and by incorporating a scheduling mechanism for actions facilitates a timely and coordinated video generation.

Video Generation Video Prediction +1

A causal view of compositional zero-shot recognition

1 code implementation NeurIPS 2020 Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik

This leads to consistent misclassification of samples from a new distribution, like new combinations of known components.

Compositional Zero-Shot Learning

Auxiliary Learning by Implicit Differentiation

1 code implementation ICLR 2021 Aviv Navon, Idan Achituve, Haggai Maron, Gal Chechik, Ethan Fetaya

Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.

Auxiliary Learning Multi-Task Learning +2

Contrastive Learning for Weakly Supervised Phrase Grounding

1 code implementation ECCV 2020 Tanmay Gupta, Arash Vahdat, Gal Chechik, Xiaodong Yang, Jan Kautz, Derek Hoiem

Given pairs of images and captions, we maximize compatibility of the attention-weighted regions and the words in the corresponding caption, compared to non-corresponding pairs of images and captions.

Contrastive Learning Language Modelling +1

From Generalized zero-shot learning to long-tail with class descriptors

1 code implementation5 Apr 2020 Dvir Samuel, Yuval Atzmon, Gal Chechik

Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes.

Few-Shot Learning Generalized Zero-Shot Learning +1

Self-Supervised Learning for Domain Adaptation on Point-Clouds

3 code implementations29 Mar 2020 Idan Achituve, Haggai Maron, Gal Chechik

Self-supervised learning (SSL) is a technique for learning useful representations from unlabeled data.

Domain Adaptation Self-Supervised Learning

Learning Object Permanence from Video

1 code implementation ECCV 2020 Aviv Shamsian, Ofri Kleinfeld, Amir Globerson, Gal Chechik

The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object.

On Learning Sets of Symmetric Elements

2 code implementations ICML 2020 Haggai Maron, Or Litany, Gal Chechik, Ethan Fetaya

We first characterize the space of linear layers that are equivariant both to element reordering and to the inherent symmetries of elements, like translation in the case of images.

3D Shape Recognition Deblurring +1

Cooperative image captioning

no code implementations26 Jul 2019 Gilad Vered, Gal Oren, Yuval Atzmon, Gal Chechik

Second, we show that the generated descriptions can be kept close to natural by constraining them to be similar to human descriptions.

Image Captioning

Few-Shot Learning with Per-Sample Rich Supervision

no code implementations10 Jun 2019 Roman Visotsky, Yuval Atzmon, Gal Chechik

Here we describe a new approach to learn with fewer samples, by using additional information that is provided per sample.

Few-Shot Learning General Classification +1

Differentiable Scene Graphs

1 code implementation26 Feb 2019 Moshiko Raboh, Roei Herzig, Gal Chechik, Jonathan Berant, Amir Globerson

In many domains, it is preferable to train systems jointly in an end-to-end manner, but SGs are not commonly used as intermediate components in visual reasoning systems because being discrete and sparse, scene-graph representations are non-differentiable and difficult to optimize.

Visual Reasoning

Informative Object Annotations: Tell Me Something I Don't Know

1 code implementation CVPR 2019 Lior Bracha, Gal Chechik

Capturing the interesting components of an image is a key aspect of image understanding.

Adaptive Confidence Smoothing for Generalized Zero-Shot Learning

no code implementations CVPR 2019 Yuval Atzmon, Gal Chechik

Specifically, our model consists of three classifiers: A "gating" model that makes soft decisions if a sample is from a "seen" class, and two experts: a ZSL expert, and an expert model for seen classes.

Generalized Zero-Shot Learning

Metric Learning for Phoneme Perception

no code implementations20 Sep 2018 Yair Lakretz, Gal Chechik, Evan-Gary Cohen, Alessandro Treves, Naama Friedmann

This study presents a new framework for learning a metric function for perceptual distances among pairs of phonemes.

Metric Learning

Probabilistic AND-OR Attribute Grouping for Zero-Shot Learning

1 code implementation7 Jun 2018 Yuval Atzmon, Gal Chechik

The soft group structure can be learned from data jointly as part of the model, and can also readily incorporate prior knowledge about groups if available.

Zero-Shot Learning

Context-aware Captions from Context-agnostic Supervision

1 code implementation CVPR 2017 Ramakrishna Vedantam, Samy Bengio, Kevin Murphy, Devi Parikh, Gal Chechik

We introduce an inference technique to produce discriminative context-aware image captions (captions that describe differences between images or visual concepts) using only generic context-agnostic training data (captions that describe a concept or an image in isolation).

Image Captioning Language Modelling

Gradual Training Method for Denoising Auto Encoders

no code implementations11 Apr 2015 Alexander Kalmanovich, Gal Chechik

Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network.

Denoising General Classification

Gradual training of deep denoising auto encoders

no code implementations19 Dec 2014 Alexander Kalmanovich, Gal Chechik

Stacked denoising auto encoders (DAEs) are well known to learn useful deep representations, which can be used to improve supervised training by initializing a deep network.

Denoising General Classification

Efficient coordinate-descent for orthogonal matrices through Givens rotations

no code implementations2 Dec 2013 Uri Shalit, Gal Chechik

Optimizing over the set of orthogonal matrices is a central component in problems like sparse-PCA or tensor decomposition.

Tensor Decomposition

Online Learning in The Manifold of Low-Rank Matrices

no code implementations NeurIPS 2010 Uri Shalit, Daphna Weinshall, Gal Chechik

When learning models that are represented in matrix forms, enforcing a low-rank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model.

Multi-Label Image Classification

An Online Algorithm for Large Scale Image Similarity Learning

no code implementations NeurIPS 2009 Gal Chechik, Uri Shalit, Varun Sharma, Samy Bengio

We describe OASIS, a method for learning pairwise similarity that is fast and scales linearly with the number of objects and the number of non-zero features.

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