2 citations · 9 across the 8 of their papers we have counts for
9 papers
MIND: Multi-Task Incremental Network Distillation
Jacopo Bonato, Francesco Pelosin, Luigi Sabetta +1
The recent surge of pervasive devices that generate dynamic data streams has underscored the necessity for learning systems to adapt continually to data distributional shifts. To t…
DUCK: Distance-based Unlearning via Centroid Kinematics
Marco Cotogni, Jacopo Bonato, Luigi Sabetta +2
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradica…
Dissecting Continual Learning a Structural and Data Analysis
Francesco Pelosin
Continual Learning (CL) is a field dedicated to devise algorithms able to achieve lifelong learning. Overcoming the knowledge disruption of previously acquired concepts, a drawback…
Simpler is Better: off-the-shelf Continual Learning Through Pretrained Backbones
Francesco Pelosin
In this short paper, we propose a baseline (off-the-shelf) for Continual Learning of Computer Vision problems, by leveraging the power of pretrained models. By doing so, we devise…
Towards Exemplar-Free Continual Learning in Vision Transformers: an Account of Attention, Functional and Weight Regularization
Francesco Pelosin, Saurav Jha, Andrea Torsello +2
In this paper, we investigate the continual learning of Vision Transformers (ViT) for the challenging exemplar-free scenario, with special focus on how to efficiently distill the k…
Smaller Is Better: An Analysis of Instance Quantity/Quality Trade-off in Rehearsal-based Continual Learning
Francesco Pelosin, Andrea Torsello
The design of machines and algorithms capable of learning in a dynamically changing environment has become an increasingly topical problem with the increase of the size and heterog…
Unsupervised semantic discovery through visual patterns detection
Francesco Pelosin, Andrea Gasparetto, Andrea Albarelli +1
We propose a new fast fully unsupervised method to discover semantic patterns. Our algorithm is able to hierarchically find visual categories and produce a segmentation mask where…
Separating Structure from Noise in Large Graphs Using the Regularity Lemma
Marco Fiorucci, Francesco Pelosin, Marcello Pelillo
How can we separate structural information from noise in large graphs? To address this fundamental question, we propose a graph summarization approach based on Szemerédi's Regulari…
Graph Compression Using The Regularity Method
Francesco Pelosin
We are living in a world which is getting more and more interconnected and, as physiological effect, the interaction between the entities produces more and more information. This h…