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Angelo Porrello
Angelo Porrello
Email verificata su unimore.it
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Citata da
Anno
Dark experience for general continual learning: a strong, simple baseline
P Buzzega, M Boschini, A Porrello, D Abati, S Calderara
Advances in Neural Information Processing Systems 33, 15920-15930, 2020
6032020
Latent space autoregression for novelty detection
D Abati, A Porrello, S Calderara, R Cucchiara
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
4852019
Rethinking Experience Replay: a Bag of Tricks for Continual Learning
P Buzzega, M Boschini, A Porrello, S Calderara
25th International Conference on Pattern Recognition (ICPR2020), 2020
972020
Class-Incremental Continual Learning into the eXtended DER-verse
M Boschini, L Bonicelli, P Buzzega, A Porrello, S Calderara
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
802022
Robust re-identification by multiple views knowledge distillation
A Porrello, L Bergamini, S Calderara
European Conference on Computer Vision, 93-110, 2020
742020
The color out of space: learning self-supervised representations for Earth Observation imagery
S Vincenzi, A Porrello, P Buzzega, M Cipriano, P Fronte, R Cuccu, ...
25th International Conference on Pattern Recognition (ICPR2020), 2020
492020
Multi-views embedding for cattle re-identification
L Bergamini, A Porrello, AC Dondona, E Del Negro, M Mattioli, N D'alterio, ...
2018 14th international conference on signal-image technology & internet …, 2018
412018
How many observations are enough? knowledge distillation for trajectory forecasting
A Monti, A Porrello, S Calderara, P Coscia, L Ballan, R Cucchiara
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
362022
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning
L Bonicelli, M Boschini, A Porrello, C Spampinato, S Calderara
Advances in Neural Information Processing Systems 35, 31886--31901, 2022
242022
And: Autoregressive novelty detectors
D Abati, A Porrello, S Calderara, R Cucchiara
arXiv preprint arXiv:1807.01653 2, 2018
222018
Input Perturbation Reduces Exposure Bias in Diffusion Models
M Ning, E Sangineto, A Porrello, S Calderara, R Cucchiara
International Conference on Machine Learning, 26245--26265, 2023
202023
Scoring pleurisy in slaughtered pigs using convolutional neural networks
AR Trachtman, L Bergamini, A Palazzi, A Porrello, ...
Veterinary research 51, 1-9, 2020
202020
Continual semi-supervised learning through contrastive interpolation consistency
M Boschini, P Buzzega, L Bonicelli, A Porrello, S Calderara
Pattern Recognition Letters 162, 9-14, 2022
192022
Transfer without Forgetting
M Boschini, L Bonicelli, A Porrello, G Bellitto, M Pennisi, S Palazzo, ...
European Conference on Computer Vision, 2022
192022
Predicting WNV circulation in Italy using earth observation data and extreme gradient boosting model
L Candeloro, C Ippoliti, F Iapaolo, F Monaco, D Morelli, R Cuccu, P Fronte, ...
Remote Sensing 12 (18), 3064, 2020
162020
Classifying signals on irregular domains via convolutional cluster pooling
A Porrello, D Abati, S Calderara, R Cucchiara
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
142019
Spotting insects from satellites: modeling the presence of Culicoides imicola through Deep CNNs
S Vincenzi, A Porrello, P Buzzega, A Conte, C Ippoliti, L Candeloro, ...
2019 15th International Conference on Signal-Image Technology & Internet …, 2019
112019
Consistency-based Self-supervised Learning for Temporal Anomaly Localization
A Panariello, A Porrello, S Calderara, R Cucchiara
European Conference on Computer Vision Workshop, 2022
72022
DAS-MIL: Distilling Across Scales for MIL classification of histological WSIs
G Bontempo, A Porrello, F Bolelli, S Calderara, E Ficarra
International Conference on Medical Image Computing and Computer-Assisted …, 2023
62023
TrackFlow: Multi-Object Tracking with Normalizing Flows
G Mancusi, A Panariello, A Porrello, M Fabbri, S Calderara, R Cucchiara
IEEE/CVF International Conference on Computer Vision, 2023
42023
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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