35 citations · 35 across the 5 of their papers we have counts for
16 papers
Generative Negative Replay for Continual Learning
Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini +1
Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems. One of the most effective strategies to control catastrophic forgetti…
International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines
Ajmal Shahbaz, Salman Khan, Mohammad Asiful Hossain +4
The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 Inter…
Continual Learning at the Edge: Real-Time Training on Smartphone Devices
Lorenzo Pellegrini, Vincenzo Lomonaco, Gabriele Graffieti +1
On-device training for personalized learning is a challenging research problem. Being able to quickly adapt deep prediction models at the edge is necessary to better suit personal…
Avalanche: an End-to-End Library for Continual Learning
Vincenzo Lomonaco, Lorenzo Pellegrini, Andrea Cossu +25
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing…
CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions
Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez +12
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…
Memory-Latency-Accuracy Trade-offs for Continual Learning on a RISC-V Extreme-Edge Node
Leonardo Ravaglia, Manuele Rusci, Alessandro Capotondi +5
AI-powered edge devices currently lack the ability to adapt their embedded inference models to the ever-changing environment. To tackle this issue, Continual Learning (CL) strategi…