29 citations · 41 across the 6 of their papers we have counts for
6 papers
Cross-Domain Transfer Learning with CoRTe: Consistent and Reliable Transfer from Black-Box to Lightweight Segmentation Model
Claudia Cuttano, Antonio Tavera, Fabio Cermelli +2
Many practical applications require training of semantic segmentation models on unlabelled datasets and their execution on low-resource hardware. Distillation from a trained source…
Domain Randomization for Robust, Affordable and Effective Closed-loop Control of Soft Robots
Gabriele Tiboni, Andrea Protopapa, Tatiana Tommasi +1
Soft robots are gaining popularity thanks to their intrinsic safety to contacts and adaptability. However, the potentially infinite number of Degrees of Freedom makes their modelin…
Bringing Online Egocentric Action Recognition into the wild
Gabriele Goletto, Mirco Planamente, Barbara Caputo +1
To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities. Egocentric vision seems to be a viable solution…
FreeREA: Training-Free Evolution-based Architecture Search
Niccolò Cavagnero, Luca Robbiano, Barbara Caputo +1
In the last decade, most research in Machine Learning contributed to the improvement of existing models, with the aim of increasing the performance of neural networks for the solut…
Online vs. Offline Adaptive Domain Randomization Benchmark
Gabriele Tiboni, Karol Arndt, Giuseppe Averta +2
Physics simulators have shown great promise for conveniently learning reinforcement learning policies in safe, unconstrained environments. However, transferring the acquired knowle…
Fault-Aware Design and Training to Enhance DNNs Reliability with Zero-Overhead
Niccolò Cavagnero, Fernando Dos Santos, Marco Ciccone +3
Deep Neural Networks (DNNs) enable a wide series of technological advancements, ranging from clinical imaging, to predictive industrial maintenance and autonomous driving. However,…