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Matteo Boschini

5 accepted papers

2024

Saliency-driven Experience Replay for Continual Learning

Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi, Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +3
NeurIPS 2024spotlight

We present Saliency-driven Experience Replay - SER - a biologically-plausible approach based on replicating human visual saliency to enhance classification models in continual learning settings. Inspired by neurophysiological evidence that the primary visual cortex does not contribute to object mani…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

Semantic Residual Prompts for Continual Learning

Martin Menabue*, Emanuele Frascaroli, Matteo Boschini, Enver Sangineto, Lorenzo Bonicelli, Angelo Porrello* +1
ECCV 2024poster

"Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and train a few parameter vectors termed prompts. Most of these methods organize these vectors in a pool of key-value pairs and use the input image as query to retrieve the prompts (values). However, as keys are lear…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2022

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello, Concetto Spampinato, Simone Calderara
NeurIPS 2022accept

Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small memory buffer; subsequently, they repeatedly optimize on the latter to prevent catastrophic forgetting. This work draws at…

Cited by 55Source ↗PDF ↗code ↗Scholar ↗
2022

Transfer without Forgetting

Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello, Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +2
ECCV 2022poster

"This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-expl…

Cited by 57Source ↗PDF ↗code ↗Scholar ↗
2020

Dark Experience for General Continual Learning: a Strong, Simple Baseline

Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, SIMONE CALDERARA
NeurIPS 2020poster

Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable. We work towards General Continual Learni…

Cited by 1149Source ↗PDF ↗code ↗Scholar ↗