Search Results for author: Pascal Frossard

Found 79 papers, 22 papers with code

Top-N: Equivariant set and graph generation without exchangeability

no code implementations5 Oct 2021 Clement Vignac, Pascal Frossard

generation in any VAE or GAN -- it is easier to train and better captures complex dependencies in the data.

Drug Discovery Graph Generation

FGOT: Graph Distances based on Filters and Optimal Transport

no code implementations9 Sep 2021 Hermina Petric Maretic, Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We tackle the problem of graph alignment by computing graph permutations that minimise our new filter distances, which implicitly solves the graph comparison problem.

Spectral Graph Convolutional Networks With Lifting-based Adaptive Graph Wavelets

no code implementations3 Aug 2021 Mingxing Xu, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

In this paper, we propose a novel class of spectral graph convolutional networks that implement graph convolutions with adaptive graph wavelets.

Graph Representation Learning

Privacy-Preserving Image Acquisition Using Trainable Optical Kernel

no code implementations28 Jun 2021 Yamin Sepehri, Pedram Pad, Pascal Frossard, L. Andrea Dunbar

Also, in contrast with the previous optical privacy-preserving methods that cannot be trained, our method is data-driven and optimized for the specific application at hand.

Message Passing in Graph Convolution Networks via Adaptive Filter Banks

no code implementations18 Jun 2021 Xing Gao, Wenrui Dai, Chenglin Li, Junni Zou, Hongkai Xiong, Pascal Frossard

Furthermore, each filter in the spectral domain corresponds to a message passing scheme, and diverse schemes are implemented via the filter bank.

Graph Classification Representation Learning

What can linearized neural networks actually say about generalization?

no code implementations12 Jun 2021 Guillermo Ortiz-Jiménez, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

For certain infinitely-wide neural networks, the neural tangent kernel (NTK) theory fully characterizes generalization, but for the networks used in practice, the empirical NTK only provides a rough first-order approximation.

SafeAMC: Adversarial training for robust modulation recognition models

no code implementations28 May 2021 Javier Maroto, Gérôme Bovet, Pascal Frossard

We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models.

A neural anisotropic view of underspecification in deep learning

no code implementations29 Apr 2021 Guillermo Ortiz-Jimenez, Itamar Franco Salazar-Reque, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we propose to study this problem from a geometric perspective with the aim to understand two key characteristics of neural network solutions in underspecified settings: how is the geometry of the learned function related to the data representation?

Fairness

Traffic signal prediction on transportation networks using spatio-temporal correlations on graphs

1 code implementation27 Apr 2021 Semin Kwak, Nikolas Geroliminis, Pascal Frossard

Multivariate time series forecasting poses challenges as the variables are intertwined in time and space, like in the case of traffic signals.

Ranked #3 on Traffic Prediction on PEMS-BAY (RMSE metric)

Multivariate Time Series Forecasting Traffic Prediction

Multilayer Graph Clustering with Optimized Node Embedding

no code implementations30 Mar 2021 Mireille El Gheche, Pascal Frossard

To do so, we propose to learn a clustering-friendly embedding of the graph nodes by solving an optimization problem that involves a fidelity term to the layers of a given multilayer graph, and a regularization on the (single-layer) graph induced by the embedding.

Graph Clustering

On the benefits of robust models in modulation recognition

no code implementations27 Mar 2021 Javier Maroto, Gérôme Bovet, Pascal Frossard

When analyzing these vulnerable models we found that adversarial perturbations do not shift the symbols towards the nearest classes in constellation space.

Image Classification

Bio-inspired Robustness: A Review

no code implementations16 Mar 2021 Harshitha Machiraju, Oh-Hyeon Choung, Pascal Frossard, Michael. H Herzog

Many studies have tried to add features of the human visual system to DCNNs to make them robust against adversarial attacks.

Self-Supervision by Prediction for Object Discovery in Videos

no code implementations9 Mar 2021 Beril Besbinar, Pascal Frossard

Despite their irresistible success, deep learning algorithms still heavily rely on annotated data.

Object Discovery Object Discovery In Videos +2

Sentence-Based Model Agnostic NLP Interpretability

1 code implementation24 Dec 2020 Yves Rychener, Xavier Renard, Djamé Seddah, Pascal Frossard, Marcin Detyniecki

Today, interpretability of Black-Box Natural Language Processing (NLP) models based on surrogates, like LIME or SHAP, uses word-based sampling to build the explanations.

Multilayer Clustered Graph Learning

no code implementations29 Oct 2020 Mireille El Gheche, Pascal Frossard

In this paper, we aim at analyzing multilayer graphs by properly combining the information provided by individual layers, while preserving the specific structure that allows us to eventually identify communities or clusters that are crucial in the analysis of graph data.

Graph Learning

FiGLearn: Filter and Graph Learning using Optimal Transport

no code implementations29 Oct 2020 Matthias Minder, Zahra Farsijani, Dhruti Shah, Mireille El Gheche, Pascal Frossard

We cast a new optimisation problem that minimises the Wasserstein distance between the distribution of the signal observations and the filtered signal distribution model.

Graph Learning

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

no code implementations19 Oct 2020 Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this article, we provide an in-depth review of the field of adversarial robustness in deep learning, and give a self-contained introduction to its main notions.

FAR: A General Framework for Attributional Robustness

no code implementations14 Oct 2020 Adam Ivankay, Ivan Girardi, Chiara Marchiori, Pascal Frossard

By borrowing notions of traditional adversarial training - a method to achieve robust predictions - we propose a novel framework for attributional robustness (FAR) to mitigate this vulnerability.

Modurec: Recommender Systems with Feature and Time Modulation

1 code implementation13 Oct 2020 Javier Maroto, Clément Vignac, Pascal Frossard

Current state of the art algorithms for recommender systems are mainly based on collaborative filtering, which exploits user ratings to discover latent factors in the data.

Collaborative Filtering Recommendation Systems

Graph signal processing for machine learning: A review and new perspectives

no code implementations31 Jul 2020 Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, Pascal Frossard

The effective representation, processing, analysis, and visualization of large-scale structured data, especially those related to complex domains such as networks and graphs, are one of the key questions in modern machine learning.

node2coords: Graph Representation Learning with Wasserstein Barycenters

no code implementations31 Jul 2020 Effrosyni Simou, Dorina Thanou, Pascal Frossard

In the decoder, the node connectivity descriptors are reconstructed as Wasserstein barycenters of the graph structural patterns.

Graph Representation Learning Node Classification

Towards robust sensing for Autonomous Vehicles: An adversarial perspective

no code implementations14 Jul 2020 Apostolos Modas, Ricardo Sanchez-Matilla, Pascal Frossard, Andrea Cavallaro

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions.

Autonomous Vehicles Decision Making

Building powerful and equivariant graph neural networks with structural message-passing

1 code implementation NeurIPS 2020 Clement Vignac, Andreas Loukas, Pascal Frossard

We address this problem and propose a powerful and equivariant message-passing framework based on two ideas: first, we propagate a one-hot encoding of the nodes, in addition to the features, in order to learn a local context matrix around each node.

Graph Regression

Graph Pooling with Node Proximity for Hierarchical Representation Learning

no code implementations19 Jun 2020 Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, Pascal Frossard

In this paper, we propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology.

Affine Transformation Graph Classification +1

Neural Anisotropy Directions

2 code implementations NeurIPS 2020 Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers.

GeoDA: a geometric framework for black-box adversarial attacks

1 code implementation CVPR 2020 Ali Rahmati, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Huaiyu Dai

We propose a geometric framework to generate adversarial examples in one of the most challenging black-box settings where the adversary can only generate a small number of queries, each of them returning the top-$1$ label of the classifier.

Wasserstein-based Graph Alignment

no code implementations12 Mar 2020 Hermina Petric Maretic, Mireille El Gheche, Matthias Minder, Giovanni Chierchia, Pascal Frossard

We propose a novel method for comparing non-aligned graphs of different sizes, based on the Wasserstein distance between graph signal distributions induced by the respective graph Laplacian matrices.

Graph Classification

Hold me tight! Influence of discriminative features on deep network boundaries

1 code implementation NeurIPS 2020 Guillermo Ortiz-Jimenez, Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

In this work, we borrow tools from the field of adversarial robustness, and propose a new perspective that relates dataset features to the distance of samples to the decision boundary.

Joint Graph-based Depth Refinement and Normal Estimation

no code implementations CVPR 2020 Mattia Rossi, Mireille El Gheche, Andreas Kuhn, Pascal Frossard

Depth estimation is an essential component in understanding the 3D geometry of a scene, with numerous applications in urban and indoor settings.

Depth Estimation

Multi-view shape estimation of transparent containers

1 code implementation27 Nov 2019 Alessio Xompero, Ricardo Sanchez-Matilla, Apostolos Modas, Pascal Frossard, Andrea Cavallaro

The 3D localisation of an object and the estimation of its properties, such as shape and dimensions, are challenging under varying degrees of transparency and lighting conditions.

Semantic Segmentation

On the choice of graph neural network architectures

2 code implementations13 Nov 2019 Clément Vignac, Guillermo Ortiz-Jiménez, Pascal Frossard

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals.

Node Classification

Imperceptible Adversarial Attacks on Tabular Data

1 code implementation8 Nov 2019 Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, Marcin Detyniecki

Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions.

Mask Combination of Multi-layer Graphs for Global Structure Inference

1 code implementation22 Oct 2019 Eda Bayram, Dorina Thanou, Elif Vural, Pascal Frossard

Structure inference is an important task for network data processing and analysis in data science.

Forward-Backward Splitting for Optimal Transport based Problems

no code implementations20 Sep 2019 Guillermo Ortiz-Jimenez, Mireille El Gheche, Effrosyni Simou, Hermina Petric Maretic, Pascal Frossard

Experiments show that the proposed method leads to a significant improvement in terms of speed and performance with respect to the state of the art for domain adaptation on a continually rotating distribution coming from the standard two moon dataset.

Domain Adaptation

iPool -- Information-based Pooling in Hierarchical Graph Neural Networks

no code implementations1 Jul 2019 Xing Gao, Hongkai Xiong, Pascal Frossard

In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs.

Graph Classification Hierarchical structure

Geometry aware convolutional filters for omnidirectional images representation

no code implementations ICLR 2019 Renata Khasanova, Pascal Frossard

In particular we propose an algorithm that adapts convolutional layers, which often serve as a core building block of a CNN, to the properties of omnidirectional images.

Autonomous Vehicles Image Classification

A geometry-inspired decision-based attack

no code implementations ICCV 2019 Yujia Liu, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

The qFool method can drastically reduce the number of queries compared to previous decision-based attacks while reaching the same quality of adversarial examples.

General Classification Image Classification

Graph heat mixture model learning

no code implementations24 Jan 2019 Hermina Petric Maretic, Mireille El Gheche, Pascal Frossard

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis.

Robustness via curvature regularization, and vice versa

1 code implementation CVPR 2019 Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Jonathan Uesato, Pascal Frossard

State-of-the-art classifiers have been shown to be largely vulnerable to adversarial perturbations.

Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

no code implementations6 Nov 2018 Arun Venkitaraman, Pascal Frossard, Saikat Chatterjee

In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph.

SparseFool: a few pixels make a big difference

1 code implementation CVPR 2019 Apostolos Modas, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data.

Image Classification

OrthoNet: Multilayer Network Data Clustering

no code implementations2 Nov 2018 Mireille El Gheche, Giovanni Chierchia, Pascal Frossard

We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space.

Graph Clustering Node Clustering

Graph Laplacian mixture model

1 code implementation23 Oct 2018 Hermina Petric Maretic, Pascal Frossard

Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets.

Graph Learning

Isometric Transformation Invariant Graph-based Deep Neural Network

no code implementations21 Aug 2018 Renata Khasanova, Pascal Frossard

In this work we present a novel Transformation Invariant Graph-based Network (TIGraNet), which learns graph-based features that are inherently invariant to isometric transformations such as rotation and translation of input images.

General Classification Translation +1

Learning graphs from data: A signal representation perspective

no code implementations3 Jun 2018 Xiaowen Dong, Dorina Thanou, Michael Rabbat, Pascal Frossard

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data.

Graph Learning

Empirical Study of the Topology and Geometry of Deep Networks

no code implementations CVPR 2018 Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto

We specifically study the topology of classification regions created by deep networks, as well as their associated decision boundary.

General Classification

Adaptive Quantization for Deep Neural Network

no code implementations4 Dec 2017 Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung, Pascal Frossard

First, we propose a measurement to estimate the effect of parameter quantization errors in individual layers on the overall model prediction accuracy.

Quantization

Graph Signal Processing: Overview, Challenges and Applications

1 code implementation1 Dec 2017 Antonio Ortega, Pascal Frossard, Jelena Kovačević, José M. F. Moura, Pierre Vandergheynst

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.

Signal Processing

Geometric robustness of deep networks: analysis and improvement

1 code implementation CVPR 2018 Can Kanbak, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

We propose ManiFool as a simple yet scalable algorithm to measure the invariance of deep networks.

Convolutional neural networks on irregular domains based on approximate vertex-domain translations

no code implementations27 Oct 2017 Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte, Dominique Pastor, Pascal Frossard

We propose a generalization of convolutional neural networks (CNNs) to irregular domains, through the use of a translation operator on a graph structure.

Translation

Graph-Based Classification of Omnidirectional Images

no code implementations26 Jul 2017 Renata Khasanova, Pascal Frossard

Omnidirectional cameras are widely used in such areas as robotics and virtual reality as they provide a wide field of view.

Classification General Classification +3

Graph learning under sparsity priors

1 code implementation18 Jul 2017 Hermina Petric Maretic, Dorina Thanou, Pascal Frossard

If this is not possible, the data structure has to be inferred from the mere signal observations.

Graph Learning

Robustness of classifiers to universal perturbations: a geometric perspective

no code implementations ICLR 2018 Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard, Stefano Soatto

Deep networks have recently been shown to be vulnerable to universal perturbations: there exist very small image-agnostic perturbations that cause most natural images to be misclassified by such classifiers.

Classification regions of deep neural networks

no code implementations26 May 2017 Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard, Stefano Soatto

The goal of this paper is to analyze the geometric properties of deep neural network classifiers in the input space.

Classification General Classification

Light Field Super-Resolution Via Graph-Based Regularization

no code implementations9 Jan 2017 Mattia Rossi, Pascal Frossard

We adopt a multi-frame alike super-resolution approach, where the complementary information in the different light field views is used to augment the spatial resolution of the whole light field.

Depth Estimation Disparity Estimation +1

Learning heat diffusion graphs

no code implementations4 Nov 2016 Dorina Thanou, Xiaowen Dong, Daniel Kressner, Pascal Frossard

Effective information analysis generally boils down to properly identifying the structure or geometry of the data, which is often represented by a graph.

Graph Learning

Universal adversarial perturbations

8 code implementations CVPR 2017 Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Omar Fawzi, Pascal Frossard

Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image-agnostic) and very small perturbation vector that causes natural images to be misclassified with high probability.

Robustness of classifiers: from adversarial to random noise

no code implementations NeurIPS 2016 Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, Pascal Frossard

Moreover, we quantify the robustness of classifiers in terms of the subspace dimension in the semi-random noise regime, and show that our bounds remarkably interpolate between the worst-case and random noise regimes.

Multi-modal image retrieval with random walk on multi-layer graphs

no code implementations12 Jul 2016 Renata Khasanova, Xiaowen Dong, Pascal Frossard

The analysis of large collections of image data is still a challenging problem due to the difficulty of capturing the true concepts in visual data.

Image Retrieval

Manitest: Are classifiers really invariant?

no code implementations23 Jul 2015 Alhussein Fawzi, Pascal Frossard

Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks.

Data Augmentation

Graph-based compression of dynamic 3D point cloud sequences

no code implementations19 Jun 2015 Dorina Thanou, Philip A. Chou, Pascal Frossard

This paper addresses the problem of compression of 3D point cloud sequences that are characterized by moving 3D positions and color attributes.

Motion Estimation

Multi-task additive models with shared transfer functions based on dictionary learning

no code implementations19 May 2015 Alhussein Fawzi, Mathieu Sinn, Pascal Frossard

Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions.

Additive models Dictionary Learning +1

Analysis of classifiers' robustness to adversarial perturbations

no code implementations9 Feb 2015 Alhussein Fawzi, Omar Fawzi, Pascal Frossard

To the best of our knowledge, our results provide the first theoretical work that addresses the phenomenon of adversarial instability recently observed for deep networks.

General Classification

Learning Laplacian Matrix in Smooth Graph Signal Representations

2 code implementations30 Jun 2014 Xiaowen Dong, Dorina Thanou, Pascal Frossard, Pierre Vandergheynst

We show that the Gaussian prior leads to an efficient representation that favors the smoothness property of the graph signals.

Graph Learning

Multiscale Event Detection in Social Media

no code implementations25 Apr 2014 Xiaowen Dong, Dimitrios Mavroeidis, Francesco Calabrese, Pascal Frossard

In this paper, we propose a novel approach towards multiscale event detection using social media data, which takes into account different temporal and spatial scales of events in the data.

Event Detection

Dictionary learning for fast classification based on soft-thresholding

no code implementations9 Feb 2014 Alhussein Fawzi, Mike Davies, Pascal Frossard

The dictionary learning problem, which jointly learns the dictionary and linear classifier, is cast as a difference of convex (DC) program and solved efficiently with an iterative DC solver.

Classification Dictionary Learning +1

A Study of Image Analysis with Tangent Distance

no code implementations11 Jan 2014 Elif Vural, Pascal Frossard

As theoretical studies about the tangent distance algorithm have been largely overlooked, we present in this work a detailed performance analysis of this useful algorithm, which can eventually help its implementation.

Image Classification Image Registration

Learning parametric dictionaries for graph signals

no code implementations5 Jan 2014 Dorina Thanou, David I Shuman, Pascal Frossard

In sparse signal representation, the choice of a dictionary often involves a tradeoff between two desirable properties -- the ability to adapt to specific signal data and a fast implementation of the dictionary.

Denoising Dictionary Learning

Analysis of Descent-Based Image Registration

no code implementations15 Feb 2013 Elif Vural, Pascal Frossard

We show that the area of this neighborhood increases at least quadratically with the smoothing filter size, which justifies the use of a smoothing step in image registration with local optimizers such as gradient descent.

Image Registration Translation

Image registration with sparse approximations in parametric dictionaries

no code implementations28 Jan 2013 Alhussein Fawzi, Pascal Frossard

We examine in this paper the problem of image registration from the new perspective where images are given by sparse approximations in parametric dictionaries of geometric functions.

Image Registration

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

1 code implementation31 Oct 2012 David I Shuman, Sunil K. Narang, Pascal Frossard, Antonio Ortega, Pierre Vandergheynst

In applications such as social, energy, transportation, sensor, and neuronal networks, high-dimensional data naturally reside on the vertices of weighted graphs.

Translation

Navigation domain representation for interactive multiview imaging

no code implementations18 Oct 2012 Thomas Maugey, Ismael Daribo, Gene Cheung, Pascal Frossard

In this paper, we propose a novel multiview data representation that permits to satisfy bandwidth and storage constraints in an interactive multiview streaming system.

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