activity
20192022
most citedIROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report

2 citations · 2 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2022

Generative Negative Replay for Continual Learning

Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini +1

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetti…

cs.LG2021

Continual Learning at the Edge: Real-Time Training on Smartphone Devices

Lorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti +1

On-device training for personalized learning is a challenging research problem. Being able to quickly adapt deep prediction models at the edge is necessary to better suit personal…

cs.LG2021

Avalanche: an End-to-End Library for Continual Learning

Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25

Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…

cs.CV20202 cited

IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report

Qi She, Fan Feng, Qi Liu +33

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~

cs.LG2019

Latent Replay for Real-Time Continual Learning

Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco +1

Training deep neural networks at the edge on light computational devices, embedded systems and robotic platforms is nowadays very challenging. Continual learning techniques, where…