Céline Hudelot

research

∙ 08/22/2023

An Analysis of Initial Training Strategies for Exemplar-Free Class-Incremental Learning

Class-Incremental Learning (CIL) aims to build classification models fro...

0 Grégoire Petit, et al. ∙

research

∙ 01/20/2023

Open-Set Likelihood Maximization for Few-Shot Learning

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...

6 Malik Boudiaf, et al. ∙

research

∙ 09/13/2022

Test-Time Adaptation with Principal Component Analysis

Machine Learning models are prone to fail when test data are different f...

0 Thomas Cordier, et al. ∙

research

∙ 06/18/2022

Model-Agnostic Few-Shot Open-Set Recognition

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classi...

5 Malik Boudiaf, et al. ∙

research

∙ 05/10/2022

Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling

Every day, a new method is published to tackle Few-Shot Image Classifica...

3 Etienne Bennequin, et al. ∙

research

∙ 02/01/2022

Minority Class Oriented Active Learning for Imbalanced Datasets

Active learning aims to optimize the dataset annotation process when res...

1 Umang Aggarwal, et al. ∙

research

∙ 02/01/2022

A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning

Deep learning approaches are successful in a wide range of AI problems a...

12 Umang Aggarwal, et al. ∙

research

∙ 01/18/2022

Optimizing Active Learning for Low Annotation Budgets

When we can not assume a large amount of annotated data , active learnin...

11 Umang Aggarwal, et al. ∙

research

∙ 11/09/2021

Improving Next-Application Prediction with Deep Personalized-Attention Neural Network

Recently, due to the ubiquity and supremacy of E-recruitment platforms, ...

0 Jun Zhu, et al. ∙

research

∙ 10/15/2021

StreaMulT: Streaming Multimodal Transformer for Heterogeneous and Arbitrary Long Sequential Data

This paper tackles the problem of processing and combining efficiently a...

0 Victor Pellegrain, et al. ∙

research

∙ 08/30/2021

Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding

Drug repurposing is more relevant than ever due to drug development's ri...

0 Edoardo Ramalli, et al. ∙

research

∙ 06/21/2021

Leveraging Conditional Generative Models in a General Explanation Framework of Classifier Decisions

Providing a human-understandable explanation of classifiers' decisions h...

0 Martin Charachon, et al. ∙

research

∙ 05/25/2021

Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift

Few-Shot Learning (FSL) algorithms have made substantial progress in lea...

6 Etienne Bennequin, et al. ∙

research

∙ 12/26/2020

Spatial Contrastive Learning for Few-Shot Classification

Existing few-shot classification methods rely to some degree on the cros...

0 Yassine Ouali, et al. ∙

research

∙ 12/21/2020

AVAE: Adversarial Variational Auto Encoder

Among the wide variety of image generative models, two models stand out:...

0 Antoine Plumerault, et al. ∙

research

∙ 12/14/2020

Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification

Explaining decisions of black-box classifiers is paramount in sensitive ...

0 Martin Charachon, et al. ∙

research

∙ 12/03/2020

Stochastic Adversarial Gradient Embedding for Active Domain Adaptation

Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a so...

0 Victor Bouvier, et al. ∙

research

∙ 07/16/2020

Autoregressive Unsupervised Image Segmentation

In this work, we propose a new unsupervised image segmentation approach ...

0 Yassine Ouali, et al. ∙

research

∙ 06/25/2020

Target Consistency for Domain Adaptation: when Robustness meets Transferability

Learning Invariant Representations has been successfully applied for rec...

0 Yassine Ouali, et al. ∙

research

∙ 06/24/2020

Robust Domain Adaptation: Representations, Weights and Inductive Bias

Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in...

0 Victor Bouvier, et al. ∙

research

∙ 06/09/2020

An Overview of Deep Semi-Supervised Learning

Deep neural networks demonstrated their ability to provide remarkable pe...

0 Yassine Ouali, et al. ∙

research

∙ 03/19/2020

Semi-Supervised Semantic Segmentation with Cross-Consistency Training

In this paper, we present a novel cross-consistency based semi-supervise...

16 Yassine Ouali, et al. ∙

research

∙ 01/28/2020

Controlling generative models with continuous factors of variations

Recent deep generative models are able to provide photo-realistic images...

12 Antoine Plumerault, et al. ∙

research

∙ 01/13/2020

Représentations lexicales pour la détection non supervisée d'événements dans un flux de tweets : étude sur des corpus français et anglais

In this work, we evaluate the performance of recent text embeddings for ...

0 Béatrice Mazoyer, et al. ∙

research

∙ 12/30/2019

A New Approach for Explainable Multiple Organ Annotation with Few Data

Despite the recent successes of deep learning, such models are still far...

40 Régis Pierrard, et al. ∙

research

∙ 07/29/2019

Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets

Learning representations which remain invariant to a nuisance factor has...

0 Victor Bouvier, et al. ∙

research

∙ 07/29/2019

Hidden Covariate Shift: A Minimal Assumption For Domain Adaptation

Unsupervised Domain Adaptation aims to learn a model on a source domain ...

0 Victor Bouvier, et al. ∙

research

∙ 10/04/2018

Learning Finer-class Networks for Universal Representations

Many real-world visual recognition use-cases can not directly benefit fr...

4 Julien Girard, et al. ∙

research

∙ 12/27/2017

Learning More Universal Representations for Transfer-Learning

Transfer learning is commonly used to address the problem of the prohibi...

0 Youssef Tamaazousti, et al. ∙

research

∙ 02/26/2015

Relaxation-based revision operators in description logics

As ontologies and description logics (DLs) reach out to a broader audien...

0 Marc Aiguier, et al. ∙

research

∙ 04/18/2013

Combinaison d'information visuelle, conceptuelle, et contextuelle pour la construction automatique de hierarchies semantiques adaptees a l'annotation d'images

This paper proposes a new methodology to automatically build semantic hi...

0 Hichem Bannour, et al. ∙

Céline Hudelot

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