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

KNIFE: Kernelized-Neural Differential Entropy Estimation

1 code implementation • 14 Feb 2022 • Georg Pichler, Pierre Colombo, Malik Boudiaf, Gunther Koliander, Pablo Piantanida

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

Domain Adaptation

Parameter-free Online Test-time Adaptation

1 code implementation • 15 Jan 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.

Mutual-Information Based Few-Shot Classification

2 code implementations • 23 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.

Classification Few-Shot Learning

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

1 code implementation • 16 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.

Few-Shot Learning

Adversarial Robustness via Fisher-Rao Regularization

1 code implementation • 12 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

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

Information Maximization for Few-Shot Learning

1 code implementation • NeurIPS 2020 • Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, Jose Dolz, Pablo Piantanida, Ismail Ben Ayed

We introduce Transductive Infomation Maximization (TIM) for few-shot learning.

Few-Shot Learning

Transductive Information Maximization For Few-Shot Learning

1 code implementation • 25 Aug 2020 • Malik Boudiaf, Ziko Imtiaz Masud, Jérôme Rony, José Dolz, Pablo Piantanida, Ismail Ben Ayed

We introduce Transductive Infomation Maximization (TIM) for few-shot learning.

Few-Shot Image Classification

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.

Metric Learning