35 citations · 37 across the 4 of their papers we have counts for
8 papers
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…
IROS 2019 Lifelong Robotic Vision Challenge -- Lifelong Object Recognition Report
Qi She, Fan Feng, Qi Liu +33
This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top finalists (out of over~…
Latent Replay for Real-Time Continual Learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco +1
Training deep neural networks at the edge on light computational devices, embedded systems and robotic platforms is nowadays very challenging. Continual learning techniques, where…
Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches
Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini
Robotic vision is a field where continual learning can play a significant role. An embodied agent operating in a complex environment subject to frequent and unpredictable changes i…
Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges
Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian +3
Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and object…