12 citations · 30 across the 16 of their papers we have counts for
4 papers · 1 filter
Detecting Morphing Attacks via Continual Incremental Training
Lorenzo Pellegrini, Guido Borghi, Annalisa Franco +1
Scenarios in which restrictions in data transfer and storage limit the possibility to compose a single dataset -- also exploiting different data sources -- to perform a batch-based…
Avalanche: A PyTorch Library for Deep Continual Learning
Antonio Carta, Lorenzo Pellegrini, Andrea Cossu +2
Continual learning is the problem of learning from a nonstationary stream of data, a fundamental issue for sustainable and efficient training of deep neural networks over time. Unf…
Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti +1
In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i.i.d. assumptions an…
Class-Incremental Learning with Repetition
Hamed Hemati, Andrea Cossu, Antonio Carta +5
Real-world data streams naturally include the repetition of previous concepts. From a Continual Learning (CL) perspective, repetition is a property of the environment and, unlike r…