Search Results for author: Lorenzo Pellegrini

Found 14 papers, 9 papers with code

Detecting Morphing Attacks via Continual Incremental Training

no code implementations27 Jul 2023 Lorenzo Pellegrini, Guido Borghi, Annalisa Franco, Davide Maltoni

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 training procedure, make the development of robust models particularly challenging.

Continual Learning

Avalanche: A PyTorch Library for Deep Continual Learning

1 code implementation2 Feb 2023 Antonio Carta, Lorenzo Pellegrini, Andrea Cossu, Hamed Hemati, Vincenzo Lomonaco

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.

Class Incremental Learning

Class-Incremental Learning with Repetition

1 code implementation26 Jan 2023 Hamed Hemati, Andrea Cossu, Antonio Carta, Julio Hurtado, Lorenzo Pellegrini, Davide Bacciu, Vincenzo Lomonaco, Damian Borth

We propose two stochastic stream generators that produce a wide range of CIR streams starting from a single dataset and a few interpretable control parameters.

Class Incremental Learning Incremental Learning

Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning

no code implementations6 Jan 2023 Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide Maltoni

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.

Class Incremental Learning Incremental Learning +1

Generative Negative Replay for Continual Learning

no code implementations12 Apr 2022 Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo Lomonaco

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems.

Continual Learning

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

1 code implementation14 Sep 2020 Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni

In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous.

Benchmarking Continual Learning

Latent Replay for Real-Time Continual Learning

3 code implementations2 Dec 2019 Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni

Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy.

Continual Learning valid

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

5 code implementations8 Jul 2019 Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini

Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.

Class Incremental Learning Object Recognition

Cannot find the paper you are looking for? You can Submit a new open access paper.