Search Results for author: Alessandro Torcinovich

Found 7 papers, 2 papers with code

The Group Loss++: A deeper look into group loss for deep metric learning

no code implementations4 Apr 2022 Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.

Clustering Image Retrieval +3

A black-box adversarial attack for poisoning clustering

1 code implementation9 Sep 2020 Antonio Emanuele Cinà, Alessandro Torcinovich, Marcello Pelillo

In an attempt to fill this gap, in this work, we propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms.

Adversarial Attack Clustering +1

The Group Loss for Deep Metric Learning

2 code implementations ECCV 2020 Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, Laura Leal-Taixe

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes.

Ranked #20 on Metric Learning on CUB-200-2011 (using extra training data)

Clustering Image Retrieval +2

Unsupervised Domain Adaptation using Graph Transduction Games

no code implementations6 May 2019 Sebastiano Vascon, Sinem Aslan, Alessandro Torcinovich, Twan van Laarhoven, Elena Marchiori, Marcello Pelillo

Unsupervised domain adaptation (UDA) amounts to assigning class labels to the unlabeled instances of a dataset from a target domain, using labeled instances of a dataset from a related source domain.

Object Recognition Unsupervised Domain Adaptation

Transductive Label Augmentation for Improved Deep Network Learning

no code implementations26 May 2018 Ismail Elezi, Alessandro Torcinovich, Sebastiano Vascon, Marcello Pelillo

Motivated by the observation that in virtually all application domains it is easy to obtain unlabeled data, in this paper we take a different perspective and propose a \emph{label augmentation} approach.

Data Augmentation General Classification +2

On the Interplay between Strong Regularity and Graph Densification

no code implementations21 Mar 2017 Marco Fiorucci, Alessandro Torcinovich, Manuel Curado, Francisco Escolano, Marcello Pelillo

In this paper we analyze the practical implications of Szemer\'edi's regularity lemma in the preservation of metric information contained in large graphs.

LEMMA

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