Christian Gagné

A Generative Model for Hallucinating Diverse Versions of Super Resolution Images

Traditionally, the main focus of image super-resolution techniques is on...
02/12/2021 ∙ by Mohamed Abderrahmen Abid, et al. ∙ 0 ∙ share

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Persistent Mixture Model Networks for Few-Shot Image Classification

We introduce Persistent Mixture Model (PMM) networks for representation ...
11/24/2020 ∙ by Arman Afrasiyabi, et al. ∙ 0 ∙ share

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

We reveal the incoherence between the widely-adopted empirical domain ad...
07/30/2020 ∙ by Changjian Shui, et al. ∙ 5 ∙ share

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Self-supervised Robust Object Detectors from Partially Labelled datasets

In the object detection task, merging various datasets from similar cont...
05/23/2020 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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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...
05/17/2020 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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Input Dropout for Spatially Aligned Modalities

Computer vision datasets containing multiple modalities such as color, d...
02/07/2020 ∙ by Sébastien de Blois, et al. ∙ 0 ∙ share

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Associative Alignment for Few-shot Image Classification

Few-shot image classification aims at training a model by using only a f...
12/11/2019 ∙ by Arman Afrasiyabi, et al. ∙ 0 ∙ share

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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...
11/25/2019 ∙ by Azadeh Sadat Mozafari, et al. ∙ 0 ∙ share

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

In this paper, we proposed a unified and principled method for both quer...
11/20/2019 ∙ by Changjian Shui, et al. ∙ 0 ∙ share

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Deep Parametric Indoor Lighting Estimation

We present a method to estimate lighting from a single image of an indoo...
10/19/2019 ∙ by Marc-André Gardner, et al. ∙ 13 ∙ share

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Toward Metrics for Differentiating Out-of-Distribution Sets

Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-o...
10/18/2019 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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

Great performances of deep learning are undeniable, with impressive resu...
05/01/2019 ∙ by Azadeh Sadat Mozafari, et al. ∙ 0 ∙ share

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A Principled Approach for Learning Task Similarity in Multitask Learning

Multitask learning aims at solving a set of related tasks simultaneously...
03/21/2019 ∙ by Changjian Shui, et al. ∙ 0 ∙ share

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Learning of Image Dehazing Models for Segmentation Tasks

To evaluate their performance, existing dehazing approaches generally re...
03/04/2019 ∙ by Sébastien de Blois, et al. ∙ 0 ∙ share

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

However Deep neural networks recently have achieved impressive results f...
10/27/2018 ∙ by Azadeh Sadat Mozafari, et al. ∙ 0 ∙ share

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Accumulating Knowledge for Lifelong Online Learning

Lifelong learning can be viewed as a continuous transfer learning proced...
10/26/2018 ∙ by Changjian Shui, et al. ∙ 0 ∙ share

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

Convolutional Neural Networks (CNNs) allowed improving the state-of-the-...
08/21/2018 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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Evaluating and Characterizing Incremental Learning from Non-Stationary Data

Incremental learning from non-stationary data poses special challenges t...
06/18/2018 ∙ by Alejandro Cervantes, et al. ∙ 0 ∙ share

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

Detection and rejection of adversarial examples in security sensitive an...
04/24/2018 ∙ by Arezoo Rajabi, et al. ∙ 0 ∙ share

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

With super-resolution optical microscopy, it is now possible to observe ...
03/28/2018 ∙ by Louis-Émile Robitaille, et al. ∙ 0 ∙ share

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Diversity regularization in deep ensembles

Calibrating the confidence of supervised learning models is important fo...
02/22/2018 ∙ by Changjian Shui, et al. ∙ 0 ∙ share

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Out-distribution training confers robustness to deep neural networks

The easiness at which adversarial instances can be generated in deep neu...
02/20/2018 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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Query Completion Using Bandits for Engines Aggregation

Assisting users by suggesting completed queries as they type is a common...
09/13/2017 ∙ by Audrey Durand, et al. ∙ 0 ∙ share

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Learning to Predict Indoor Illumination from a Single Image

We propose an automatic method to infer high dynamic range illumination ...
04/01/2017 ∙ by Marc-André Gardner, et al. ∙ 0 ∙ share

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Robustness to Adversarial Examples through an Ensemble of Specialists

We are proposing to use an ensemble of diverse specialists, where specia...
02/22/2017 ∙ by Mahdieh Abbasi, et al. ∙ 0 ∙ share

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Estimating Quality in Multi-Objective Bandits Optimization

Many real-world applications are characterized by a number of conflictin...
01/04/2017 ∙ by Audrey Durand, et al. ∙ 0 ∙ share

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Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks

The storage and computation requirements of Convolutional Neural Network...
11/05/2016 ∙ by Farkhondeh Kiaee, et al. ∙ 0 ∙ share

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Sustainable Cooperative Coevolution with a Multi-Armed Bandit

This paper proposes a self-adaptation mechanism to manage the resources ...
04/10/2013 ∙ by François-Michel De Rainville, et al. ∙ 0 ∙ share

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Christian Gagné

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