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 attention to a hidden pitfall of this widespread practice: repeated optimization on a small pool of data inevitably leads to tight and unstable decision boundaries, which are a major hindrance to generalization. To address this issue, we propose Lipschitz-DrivEn Rehearsal (LiDER), a surrogate objective that induces smoothness in the backbone network by constraining its layer-wise Lipschitz constants w.r.t. replay examples. By means of extensive experiments, we show that applying LiDER delivers a stable performance gain to several state-of-the-art rehearsal CL methods across multiple datasets, both in the presence and absence of pre-training. Through additional ablative experiments, we highlight peculiar aspects of buffer overfitting in CL and better characterize the effect produced by LiDER. Code is available at https://github.com/aimagelab/LiDER

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning / Bonicelli, Lorenzo; Boschini, Matteo; Porrello, Angelo; Spampinato, Concetto; Calderara, Simone. - (2022). ((Intervento presentato al convegno Thirty-sixth Conference on Neural Information Processing Systems tenutosi a New Orleans nel 28 Nov 2022 - 09 Dec 2022.

On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning

Lorenzo Bonicelli;Matteo Boschini;Angelo Porrello;Simone Calderara
2022-01-01

Abstract

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 attention to a hidden pitfall of this widespread practice: repeated optimization on a small pool of data inevitably leads to tight and unstable decision boundaries, which are a major hindrance to generalization. To address this issue, we propose Lipschitz-DrivEn Rehearsal (LiDER), a surrogate objective that induces smoothness in the backbone network by constraining its layer-wise Lipschitz constants w.r.t. replay examples. By means of extensive experiments, we show that applying LiDER delivers a stable performance gain to several state-of-the-art rehearsal CL methods across multiple datasets, both in the presence and absence of pre-training. Through additional ablative experiments, we highlight peculiar aspects of buffer overfitting in CL and better characterize the effect produced by LiDER. Code is available at https://github.com/aimagelab/LiDER
2022
Thirty-sixth Conference on Neural Information Processing Systems
New Orleans
28 Nov 2022 - 09 Dec 2022
Bonicelli, Lorenzo; Boschini, Matteo; Porrello, Angelo; Spampinato, Concetto; Calderara, Simone
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning / Bonicelli, Lorenzo; Boschini, Matteo; Porrello, Angelo; Spampinato, Concetto; Calderara, Simone. - (2022). ((Intervento presentato al convegno Thirty-sixth Conference on Neural Information Processing Systems tenutosi a New Orleans nel 28 Nov 2022 - 09 Dec 2022.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1289324
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