Christian Gagné

research

∙ 05/08/2023

Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning

Generally, image-to-image translation (i2i) methods aim at learning mapp...

0 Mohamed Abid, et al. ∙

research

∙ 03/04/2023

Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes

Neural network ensembles have been studied extensively in the context of...

0 Thomas Philippon, et al. ∙

research

∙ 10/19/2022

On Learning Fairness and Accuracy on Multiple Subgroups

We propose an analysis in fair learning that preserves the utility of th...

1 Changjian Shui, et al. ∙

research

∙ 05/26/2022

Fair Representation Learning through Implicit Path Alignment

We consider a fair representation learning perspective, where optimal pr...

1 Changjian Shui, et al. ∙

research

∙ 04/02/2022

Matching Feature Sets for Few-Shot Image Classification

In image classification, it is common practice to train deep networks to...

0 Arman Afrasiyabi, et al. ∙

research

∙ 07/23/2021

Image-to-Image Translation with Low Resolution Conditioning

Most image-to-image translation methods focus on learning mappings acros...

0 Mohamed Abderrahmen Abid, et al. ∙

research

∙ 05/30/2021

On the benefits of representation regularization in invariance based domain generalization

A crucial aspect in reliable machine learning is to design a deployable ...

0 Changjian Shui, et al. ∙

research

∙ 05/09/2021

Aggregating From Multiple Target-Shifted Sources

Multi-source domain adaptation aims at leveraging the knowledge from mul...

0 Changjian Shui, et al. ∙

research

∙ 03/05/2021

Meta Learning Black-Box Population-Based Optimizers

The no free lunch theorem states that no model is better suited to every...

0 Hugo Siqueira Gomes, et al. ∙

research

∙ 02/12/2021

A Generative Model for Hallucinating Diverse Versions of Super Resolution Images

Traditionally, the main focus of image super-resolution techniques is on...

0 Mohamed Abderrahmen Abid, et al. ∙

research

∙ 11/24/2020

Persistent Mixture Model Networks for Few-Shot Image Classification

We introduce Persistent Mixture Model (PMM) networks for representation ...

0 Arman Afrasiyabi, et al. ∙

research

∙ 07/30/2020

Beyond ℋ-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence

We reveal the incoherence between the widely-adopted empirical domain ad...

5 Changjian Shui, et al. ∙

research

∙ 05/23/2020

Self-supervised Robust Object Detectors from Partially Labelled datasets

In the object detection task, merging various datasets from similar cont...

0 Mahdieh Abbasi, et al. ∙

research

∙ 05/17/2020

Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks

We aim at demonstrating the influence of diversity in the ensemble of CN...

0 Mahdieh Abbasi, et al. ∙

research

∙ 02/07/2020

Input Dropout for Spatially Aligned Modalities

Computer vision datasets containing multiple modalities such as color, d...

0 Sébastien de Blois, et al. ∙

research

∙ 12/11/2019

Associative Alignment for Few-shot Image Classification

Few-shot image classification aims at training a model by using only a f...

0 Arman Afrasiyabi, et al. ∙

research

∙ 11/25/2019

A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift

The uncertainty estimation is critical in real-world decision making app...

0 Azadeh Sadat Mozafari, et al. ∙

research

∙ 11/20/2019

Deep Active Learning: Unified and Principled Method for Query and Training

In this paper, we proposed a unified and principled method for both quer...

0 Changjian Shui, et al. ∙

research

∙ 10/19/2019

Deep Parametric Indoor Lighting Estimation

We present a method to estimate lighting from a single image of an indoo...

13 Marc-André Gardner, et al. ∙

research

∙ 10/18/2019

Toward Metrics for Differentiating Out-of-Distribution Sets

Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-o...

0 Mahdieh Abbasi, et al. ∙

research

∙ 05/01/2019

Unsupervised Temperature Scaling: Post-Processing Unsupervised Calibration of Deep Models Decisions

Great performances of deep learning are undeniable, with impressive resu...

0 Azadeh Sadat Mozafari, et al. ∙

research

∙ 03/21/2019

A Principled Approach for Learning Task Similarity in Multitask Learning

Multitask learning aims at solving a set of related tasks simultaneously...

0 Changjian Shui, et al. ∙

research

∙ 03/04/2019

Learning of Image Dehazing Models for Segmentation Tasks

To evaluate their performance, existing dehazing approaches generally re...

0 Sébastien de Blois, et al. ∙

research

∙ 10/27/2018

A New Loss Function for Temperature Scaling to have Better Calibrated Deep Networks

However Deep neural networks recently have achieved impressive results f...

0 Azadeh Sadat Mozafari, et al. ∙

research

∙ 10/26/2018

Accumulating Knowledge for Lifelong Online Learning

Lifelong learning can be viewed as a continuous transfer learning proced...

0 Changjian Shui, et al. ∙

research

∙ 08/21/2018

Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection

Convolutional Neural Networks (CNNs) allowed improving the state-of-the-...

0 Mahdieh Abbasi, et al. ∙

research

∙ 06/18/2018

Evaluating and Characterizing Incremental Learning from Non-Stationary Data

Incremental learning from non-stationary data poses special challenges t...

0 Alejandro Cervantes, et al. ∙

research

∙ 04/24/2018

Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning

Detection and rejection of adversarial examples in security sensitive an...

0 Arezoo Rajabi, et al. ∙

research

∙ 03/28/2018

Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy

With super-resolution optical microscopy, it is now possible to observe ...

0 Louis-Émile Robitaille, et al. ∙

research

∙ 02/22/2018

Diversity regularization in deep ensembles

Calibrating the confidence of supervised learning models is important fo...

0 Changjian Shui, et al. ∙

research

∙ 02/20/2018

Out-distribution training confers robustness to deep neural networks

The easiness at which adversarial instances can be generated in deep neu...

0 Mahdieh Abbasi, et al. ∙

research

∙ 09/13/2017

Query Completion Using Bandits for Engines Aggregation

Assisting users by suggesting completed queries as they type is a common...

0 Audrey Durand, et al. ∙

research

∙ 04/01/2017

Learning to Predict Indoor Illumination from a Single Image

We propose an automatic method to infer high dynamic range illumination ...

0 Marc-André Gardner, et al. ∙

research

∙ 02/22/2017

Robustness to Adversarial Examples through an Ensemble of Specialists

We are proposing to use an ensemble of diverse specialists, where specia...

0 Mahdieh Abbasi, et al. ∙

research

∙ 01/04/2017

Estimating Quality in Multi-Objective Bandits Optimization

Many real-world applications are characterized by a number of conflictin...

0 Audrey Durand, et al. ∙

research

∙ 11/05/2016

Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks

The storage and computation requirements of Convolutional Neural Network...

0 Farkhondeh Kiaee, et al. ∙

research

∙ 04/10/2013

Sustainable Cooperative Coevolution with a Multi-Armed Bandit

This paper proposes a self-adaptation mechanism to manage the resources ...

0 François-Michel De Rainville, et al. ∙

Christian Gagné

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