244 citations · 250 across the 10 of their papers we have counts for
14 papers
Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models
Pierre Colombo, Victor Pellegrain, Malik Boudiaf +5
Proprietary and closed APIs are becoming increasingly common to process natural language, and are impacting the practical applications of natural language processing, including few…
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models
Alexander G. Reisach, Myriam Tami, Christof Seiler +2
Additive Noise Models (ANMs) are a common model class for causal discovery from observational data and are often used to generate synthetic data for causal discovery benchmarking.…
Open-Set Likelihood Maximization for Few-Shot Learning
Malik Boudiaf, Etienne Bennequin, Myriam Tami +4
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have a few labeled samples, while simultaneously de…
Model-Agnostic Few-Shot Open-Set Recognition
Malik Boudiaf, Etienne Bennequin, Myriam Tami +4
We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, i.e. classifying instances among a set of classes for which we only have few labeled samples, while simultaneously dete…
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Etienne Bennequin, Myriam Tami, Antoine Toubhans +1
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these cur…
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift
Etienne Bennequin, Victor Bouvier, Myriam Tami +2
Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes en…
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) aims to bridge the gap between a source domain, where labelled data are available, and a target domain only represented with unlabelled data. I…
Spatial Contrastive Learning for Few-Shot Classification
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we explore contrastive learning for few-shot classification, in which we propose to use it as an additional auxiliary training objective acting as a data-dependent r…
Autoregressive Unsupervised Image Segmentation
Yassine Ouali, Céline Hudelot, Myriam Tami
In this work, we propose a new unsupervised image segmentation approach based on mutual information maximization between different constructed views of the inputs. Taking inspirati…
An Overview of Deep Semi-Supervised Learning
Yassine Ouali, Céline Hudelot, Myriam Tami
Deep neural networks demonstrated their ability to provide remarkable performances on a wide range of supervised learning tasks (e.g., image classification) when trained on extensi…
Target Consistency for Domain Adaptation: when Robustness meets Transferability
Yassine Ouali, Victor Bouvier, Myriam Tami +1
Learning Invariant Representations has been successfully applied for reconciling a source and a target domain for Unsupervised Domain Adaptation. By investigating the robustness of…
Robust Domain Adaptation: Representations, Weights and Inductive Bias
Victor Bouvier, Philippe Very, Clément Chastagnol +2
Unsupervised Domain Adaptation (UDA) has attracted a lot of attention in the last ten years. The emergence of Domain Invariant Representations (IR) has improved drastically the tra…
Semi-Supervised Semantic Segmentation with Cross-Consistency Training
Yassine Ouali, Céline Hudelot, Myriam Tami
In this paper, we present a novel cross-consistency based semi-supervised approach for semantic segmentation. Consistency training has proven to be a powerful semi-supervised learn…
Uncertain Trees: Dealing with Uncertain Inputs in Regression Trees
Myriam Tami, Marianne Clausel, Emilie Devijver +4
Tree-based ensemble methods, as Random Forests and Gradient Boosted Trees, have been successfully used for regression in many applications and research studies. Furthermore, these…