Search Results for author: Malik Boudiaf

Found 20 papers, 16 papers with code

Mutual-Information Based Few-Shot Classification

3 code implementations23 Jun 2021 Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida

We motivate our transductive loss by deriving a formal relation between the classification accuracy and mutual-information maximization.

Benchmarking Classification +1

A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses

1 code implementation ECCV 2020 Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, Ismail Ben Ayed

Second, we show that, more generally, minimizing the cross-entropy is actually equivalent to maximizing the mutual information, to which we connect several well-known pairwise losses.

Ranked #12 on Metric Learning on CARS196 (using extra training data)

Metric Learning

Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?

2 code implementations CVPR 2021 Malik Boudiaf, Hoel Kervadec, Ziko Imtiaz Masud, Pablo Piantanida, Ismail Ben Ayed, Jose Dolz

We show that the way inference is performed in few-shot segmentation tasks has a substantial effect on performances -- an aspect often overlooked in the literature in favor of the meta-learning paradigm.

Few-Shot Semantic Segmentation

Parameter-free Online Test-time Adaptation

1 code implementation CVPR 2022 Malik Boudiaf, Romain Mueller, Ismail Ben Ayed, Luca Bertinetto

An interesting and practical paradigm is online test-time adaptation, according to which training data is inaccessible, no labelled data from the test distribution is available, and adaptation can only happen at test time and on a handful of samples.

Test-time Adaptation

Model-Agnostic Few-Shot Open-Set Recognition

1 code implementation18 Jun 2022 Malik Boudiaf, Etienne Bennequin, Myriam Tami, Celine Hudelot, Antoine Toubhans, Pablo Piantanida, Ismail Ben Ayed

Through extensive experiments spanning 5 datasets, we show that OSTIM surpasses both inductive and existing transductive methods in detecting open-set instances while competing with the strongest transductive methods in classifying closed-set instances.

Few-Shot Learning Open Set Learning

Open-Set Likelihood Maximization for Few-Shot Learning

1 code implementation CVPR 2023 Malik Boudiaf, Etienne Bennequin, Myriam Tami, Antoine Toubhans, Pablo Piantanida, Céline Hudelot, Ismail Ben Ayed

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 detecting instances that do not belong to any known class.

Few-Shot Image Classification Few-Shot Learning +2

Realistic Evaluation of Transductive Few-Shot Learning

1 code implementation NeurIPS 2021 Olivier Veilleux, Malik Boudiaf, Pablo Piantanida, Ismail Ben Ayed

Transductive inference is widely used in few-shot learning, as it leverages the statistics of the unlabeled query set of a few-shot task, typically yielding substantially better performances than its inductive counterpart.

Few-Shot Learning

A Differential Entropy Estimator for Training Neural Networks

1 code implementation14 Feb 2022 Georg Pichler, Pierre Colombo, Malik Boudiaf, Günther Koliander, Pablo Piantanida

Mutual Information (MI) has been widely used as a loss regularizer for training neural networks.

Domain Adaptation

Transductive Few-Shot Learning: Clustering is All You Need?

1 code implementation16 Jun 2021 Imtiaz Masud Ziko, Malik Boudiaf, Jose Dolz, Eric Granger, Ismail Ben Ayed

Surprisingly, we found that even standard clustering procedures (e. g., K-means), which correspond to particular, non-regularized cases of our general model, already achieve competitive performances in comparison to the state-of-the-art in few-shot learning.

Clustering Few-Shot Learning

Bag of Tricks for Fully Test-Time Adaptation

1 code implementation3 Oct 2023 Saypraseuth Mounsaveng, Florent Chiaroni, Malik Boudiaf, Marco Pedersoli, Ismail Ben Ayed

Fully Test-Time Adaptation (TTA), which aims at adapting models to data drifts, has recently attracted wide interest.

Test-time Adaptation

Simplex Clustering via sBeta with Applications to Online Adjustment of Black-Box Predictions

1 code implementation30 Jul 2022 Florent Chiaroni, Malik Boudiaf, Amar Mitiche, Ismail Ben Ayed

We explore clustering the softmax predictions of deep neural networks and introduce a novel probabilistic clustering method, referred to as k-sBetas.

Clustering Descriptive +1

Adversarial Robustness via Fisher-Rao Regularization

1 code implementation12 Jun 2021 Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, Pablo Piantanida

Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle.

Adversarial Defense Adversarial Robustness

Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference

1 code implementation26 Oct 2022 Ségolène Martin, Malik Boudiaf, Emilie Chouzenoux, Jean-Christophe Pesquet, Ismail Ben Ayed

We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes.

FHIST: A Benchmark for Few-shot Classification of Histological Images

no code implementations31 May 2022 Fereshteh Shakeri, Malik Boudiaf, Sina Mohammadi, Ivaxi Sheth, Mohammad Havaei, Ismail Ben Ayed, Samira Ebrahimi Kahou

We build few-shot tasks and base-training data with various tissue types, different levels of domain shifts stemming from various cancer sites, and different class-granularity levels, thereby reflecting realistic scenarios.

Classification Few-Shot Learning +1

In Search for a Generalizable Method for Source Free Domain Adaptation

no code implementations13 Feb 2023 Malik Boudiaf, Tom Denton, Bart van Merriënboer, Vincent Dumoulin, Eleni Triantafillou

Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data.

Source-Free Domain Adaptation

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models

no code implementations21 Oct 2023 Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Victor Storchan, Myriam Tami, Ismail Ben Ayed, Celine Hudelot, Pablo Piantanida

First, we introduce a scenario where the embedding of a pre-trained model is served through a gated API with compute-cost and data-privacy constraints.

Classification Transductive Learning

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