no code implementations • 25 Feb 2024 • Andrea Maracani, Raffaello Camoriano, Elisa Maiettini, Davide Talon, Lorenzo Rosasco, Lorenzo Natale
This study provides a comprehensive benchmark framework for Source-Free Unsupervised Domain Adaptation (SF-UDA) in image classification, aiming to achieve a rigorous empirical understanding of the complex relationships between multiple key design factors in SF-UDA methods.
1 code implementation • 2 Nov 2023 • Gabriele M. Caddeo, Andrea Maracani, Paolo D. Alfano, Nicola A. Piga, Lorenzo Rosasco, Lorenzo Natale
Our evaluation is conducted on a dataset of tactile images obtained from a set of ten 3D printed YCB objects.
no code implementations • 19 Sep 2023 • Chang Liu, Giulia Rizzoli, Francesco Barbato, Andrea Maracani, Marco Toldo, Umberto Michieli, Yi Niu, Pietro Zanuttigh
Catastrophic forgetting of previous knowledge is a critical issue in continual learning typically handled through various regularization strategies.
no code implementations • 10 Feb 2023 • Andrea Maracani, Raffaello Camoriano, Elisa Maiettini, Davide Talon, Lorenzo Rosasco, Lorenzo Natale
Fine-tuning and Domain Adaptation emerged as effective strategies for efficiently transferring deep learning models to new target tasks.
1 code implementation • ICCV 2021 • Andrea Maracani, Umberto Michieli, Marco Toldo, Pietro Zanuttigh
Replay data are then blended with new samples during the incremental steps.
no code implementations • 28 Dec 2020 • Elisa Maiettini, Andrea Maracani, Raffaello Camoriano, Giulia Pasquale, Vadim Tikhanoff, Lorenzo Rosasco, Lorenzo Natale
We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning).
no code implementations • 21 May 2020 • Marco Toldo, Andrea Maracani, Umberto Michieli, Pietro Zanuttigh
The aim of this paper is to give an overview of the recent advancements in the Unsupervised Domain Adaptation (UDA) of deep networks for semantic segmentation.