127 citations · 282 across the 12 of their papers we have counts for
12 papers
An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini +3
The field of Continual Learning (CL) has inspired numerous researchers over the years, leading to increasingly advanced countermeasures to the issue of catastrophic forgetting. Mos…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…
Semantic Residual Prompts for Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini +4
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…
On the Effectiveness of Equivariant Regularization for Robust Online Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Emanuele Frascaroli +6
Humans can learn incrementally, whereas neural networks forget previously acquired information catastrophically. Continual Learning (CL) approaches seek to bridge this gap by facil…
Latent Spectral Regularization for Continual Learning
Emanuele Frascaroli, Riccardo Benaglia, Matteo Boschini +4
While biological intelligence grows organically as new knowledge is gathered throughout life, Artificial Neural Networks forget catastrophically whenever they face a changing train…
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
Lorenzo Bonicelli, Matteo Boschini, Angelo Porrello +2
Rehearsal approaches enjoy immense popularity with Continual Learning (CL) practitioners. These methods collect samples from previously encountered data distributions in a small me…
Transfer without Forgetting
Matteo Boschini, Lorenzo Bonicelli, Angelo Porrello +5
This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretrainin…
Effects of Auxiliary Knowledge on Continual Learning
Giovanni Bellitto, Matteo Pennisi, Simone Palazzo +4
In Continual Learning (CL), a neural network is trained on a stream of data whose distribution changes over time. In this context, the main problem is how to learn new information…
Class-Incremental Continual Learning into the eXtended DER-verse
Matteo Boschini, Lorenzo Bonicelli, Pietro Buzzega +2
The staple of human intelligence is the capability of acquiring knowledge in a continuous fashion. In stark contrast, Deep Networks forget catastrophically and, for this reason, th…
Continual Semi-Supervised Learning through Contrastive Interpolation Consistency
Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli +2
Continual Learning (CL) investigates how to train Deep Networks on a stream of tasks without incurring forgetting. CL settings proposed in literature assume that every incoming exa…
Rethinking Experience Replay: a Bag of Tricks for Continual Learning
Pietro Buzzega, Matteo Boschini, Angelo Porrello +1
In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time. Under these assumptions, it is especially challenging to improve on clas…
Dark Experience for General Continual Learning: a Strong, Simple Baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello +2
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 str…