1 citations · 1 across the 6 of their papers we have counts for
6 papers
On-Device Learning with Binary Neural Networks
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Existing Continual Learning (CL) solutions only partially address the constraints on power, memory and computation of the deep learning models when deployed on low-power embedded C…
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…
Input Layer Binarization with Bit-Plane Encoding
Lorenzo Vorabbi, Davide Maltoni, Stefano Santi
Binary Neural Networks (BNNs) use 1-bit weights and activations to efficiently execute deep convolutional neural networks on edge devices. Nevertheless, the binarization of the fir…
Region Prediction for Efficient Robot Localization on Large Maps
Matteo Scucchia, Davide Maltoni
Recognizing already explored places (a.k.a. place recognition) is a fundamental task in Simultaneous Localization and Mapping (SLAM) to enable robot relocalization and loop closure…
On the challenges to learn from Natural Data Streams
Guido Borghi, Gabriele Graffieti, Davide Maltoni
In real-world contexts, sometimes data are available in form of Natural Data Streams, i.e. data characterized by a streaming nature, unbalanced distribution, data drift over a long…
Is Class-Incremental Enough for Continual Learning?
Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini +4
The ability of a model to learn continually can be empirically assessed in different continual learning scenarios. Each scenario defines the constraints and the opportunities of th…