Search Results for author: Angelo Porrello

Found 14 papers, 8 papers with code

Spotting Virus from Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks

no code implementations7 Sep 2022 Lorenzo Bonicelli, Angelo Porrello, Stefano Vincenzi, Carla Ippoliti, Federica Iapaolo, Annamaria Conte, Simone Calderara

In this paper, we seek to predict WNV circulation by feeding Deep Neural Networks (DNNs) with satellite images, which have been extensively shown to hold environmental and climatic features.

Graph Attention

Consistency-based Self-supervised Learning for Temporal Anomaly Localization

no code implementations10 Aug 2022 Aniello Panariello, Angelo Porrello, Simone Calderara, Rita Cucchiara

This work tackles Weakly Supervised Anomaly detection, in which a predictor is allowed to learn not only from normal examples but also from a few labeled anomalies made available during training.

Anomaly Detection In Surveillance Videos Self-Supervised Learning +1

How many Observations are Enough? Knowledge Distillation for Trajectory Forecasting

no code implementations CVPR 2022 Alessio Monti, Angelo Porrello, Simone Calderara, Pasquale Coscia, Lamberto Ballan, Rita Cucchiara

To this end, we conceive a novel distillation strategy that allows a knowledge transfer from a teacher network to a student one, the latter fed with fewer observations (just two ones).

Knowledge Distillation Trajectory Forecasting +1

Continual Semi-Supervised Learning through Contrastive Interpolation Consistency

1 code implementation14 Aug 2021 Matteo Boschini, Pietro Buzzega, Lorenzo Bonicelli, Angelo Porrello, Simone Calderara

This work explores Continual Semi-Supervised Learning (CSSL): here, only a small fraction of labeled input examples are shown to the learner.

Continual Learning Metric Learning

Rethinking Experience Replay: a Bag of Tricks for Continual Learning

2 code implementations12 Oct 2020 Pietro Buzzega, Matteo Boschini, Angelo Porrello, Simone Calderara

In Continual Learning, a Neural Network is trained on a stream of data whose distribution shifts over time.

Continual Learning

The color out of space: learning self-supervised representations for Earth Observation imagery

1 code implementation22 Jun 2020 Stefano Vincenzi, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, Simone Calderara

We conduct experiments on land cover classification (BigEarthNet) and West Nile Virus detection, showing that colorization is a solid pretext task for training a feature extractor.

Colorization Disease Prediction +3

Dark Experience for General Continual Learning: a Strong, Simple Baseline

2 code implementations NeurIPS 2020 Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, Simone Calderara

Continual Learning has inspired a plethora of approaches and evaluation settings; however, the majority of them overlooks the properties of a practical scenario, where the data stream cannot be shaped as a sequence of tasks and offline training is not viable.

Continual Learning Knowledge Distillation

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

no code implementations13 Feb 2019 Angelo Porrello, Davide Abati, Simone Calderara, Rita Cucchiara

We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset.

General Classification

Multi-views Embedding for Cattle Re-identification

no code implementations13 Feb 2019 Luca Bergamini, Angelo Porrello, Andrea Capobianco Dondona, Ercole Del Negro, Mauro Mattioli, Nicola D'Alterio, Simone Calderara

People re-identification task has seen enormous improvements in the latest years, mainly due to the development of better image features extraction from deep Convolutional Neural Networks (CNN) and the availability of large datasets.

Latent Space Autoregression for Novelty Detection

1 code implementation CVPR 2019 Davide Abati, Angelo Porrello, Simone Calderara, Rita Cucchiara

Novelty detection is commonly referred to as the discrimination of observations that do not conform to a learned model of regularity.

Anomaly Detection

Cannot find the paper you are looking for? You can Submit a new open access paper.