Search Results for author: Imtiaz Masud Ziko

Found 5 papers, 5 papers with code

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.

Few-Shot Learning

Laplacian Regularized Few-Shot Learning

1 code implementation28 Jun 2020 Imtiaz Masud Ziko, Jose Dolz, Eric Granger, Ismail Ben Ayed

Our transductive inference does not re-train the base model, and can be viewed as a graph clustering of the query set, subject to supervision constraints from the support set.

Few-Shot Image Classification Graph Clustering

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 #7 on Metric Learning on In-Shop (using extra training data)

Metric Learning

Variational Fair Clustering

1 code implementation19 Jun 2019 Imtiaz Masud Ziko, Eric Granger, Jing Yuan, Ismail Ben Ayed

We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker's inequality.

Fairness

Scalable Laplacian K-modes

1 code implementation NeurIPS 2018 Imtiaz Masud Ziko, Eric Granger, Ismail Ben Ayed

Furthermore, we show that the density modes can be obtained as byproducts of the assignment variables via simple maximum-value operations whose additional computational cost is linear in the number of data points.

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