Search Results for author: Davide Maltoni

Found 19 papers, 10 papers with code

On the challenges to learn from Natural Data Streams

no code implementations9 Jan 2023 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 time frame and strong correlation of samples in short time ranges.

Incremental Learning

Architect, Regularize and Replay (ARR): a Flexible Hybrid Approach for Continual Learning

no code implementations6 Jan 2023 Vincenzo Lomonaco, Lorenzo Pellegrini, Gabriele Graffieti, Davide Maltoni

In recent years we have witnessed a renewed interest in machine learning methodologies, especially for deep representation learning, that could overcome basic i. i. d.

class-incremental learning Incremental Learning +1

Morphing Attack Potential

1 code implementation28 Apr 2022 Matteo Ferrara, Annalisa Franco, Davide Maltoni, Christoph Busch

In security systems the risk assessment in the sense of common criteria testing is a very relevant topic; this requires quantifying the attack potential in terms of the expertise of the attacker, his knowledge about the target and access to equipment.

Face Recognition

Generative Negative Replay for Continual Learning

no code implementations12 Apr 2022 Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, Vincenzo Lomonaco

Learning continually is a key aspect of intelligence and a necessary ability to solve many real-life problems.

Continual Learning

Is Class-Incremental Enough for Continual Learning?

no code implementations6 Dec 2021 Andrea Cossu, Gabriele Graffieti, Lorenzo Pellegrini, Davide Maltoni, Davide Bacciu, Antonio Carta, Vincenzo Lomonaco

The ability of a model to learn continually can be empirically assessed in different continual learning scenarios.

Continual Learning

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

1 code implementation14 Sep 2020 Vincenzo Lomonaco, Lorenzo Pellegrini, Pau Rodriguez, Massimo Caccia, Qi She, Yu Chen, Quentin Jodelet, Ruiping Wang, Zheda Mai, David Vazquez, German I. Parisi, Nikhil Churamani, Marc Pickett, Issam Laradji, Davide Maltoni

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 more adaptive, efficient and autonomous.

Continual Learning

Latent Replay for Real-Time Continual Learning

3 code implementations2 Dec 2019 Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, Davide Maltoni

Continual learning techniques, where complex models are incrementally trained on small batches of new data, can make the learning problem tractable even for CPU-only embedded devices enabling remarkable levels of adaptiveness and autonomy.

Continual Learning

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

4 code implementations8 Jul 2019 Vincenzo Lomonaco, Davide Maltoni, Lorenzo Pellegrini

Ideally, continual learning should be triggered by the availability of short videos of single objects and performed on-line on on-board hardware with fine-grained updates.

Continual Learning Object Recognition

Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges

no code implementations29 Jun 2019 Timothée Lesort, Vincenzo Lomonaco, Andrei Stoian, Davide Maltoni, David Filliat, Natalia Díaz-Rodríguez

An important challenge for machine learning is not necessarily finding solutions that work in the real world but rather finding stable algorithms that can learn in real world.

BIG-bench Machine Learning Continual Learning

Continual Reinforcement Learning in 3D Non-stationary Environments

1 code implementation24 May 2019 Vincenzo Lomonaco, Karan Desai, Eugenio Culurciello, Davide Maltoni

High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques.

reinforcement-learning reinforcement Learning

Face morphing detection in the presence of printing/scanning and heterogeneous image sources

no code implementations25 Jan 2019 Matteo Ferrara, Annalisa Franco, Davide Maltoni

Face morphing represents nowadays a big security threat in the context of electronic identity documents as well as an interesting challenge for researchers in the field of face recognition.

Data Augmentation Face Recognition

Don't forget, there is more than forgetting: new metrics for Continual Learning

no code implementations31 Oct 2018 Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat, Davide Maltoni

Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex knowledge and skills.

Continual Learning Transfer Learning

Continuous Learning in Single-Incremental-Task Scenarios

1 code implementation22 Jun 2018 Davide Maltoni, Vincenzo Lomonaco

It was recently shown that architectural, regularization and rehearsal strategies can be used to train deep models sequentially on a number of disjoint tasks without forgetting previously acquired knowledge.

class-incremental learning Incremental Learning

CORe50: a New Dataset and Benchmark for Continuous Object Recognition

1 code implementation9 May 2017 Vincenzo Lomonaco, Davide Maltoni

Continuous/Lifelong learning of high-dimensional data streams is a challenging research problem.

Continuous Object Recognition

Semi-supervised Tuning from Temporal Coherence

1 code implementation10 Nov 2015 Davide Maltoni, Vincenzo Lomonaco

Recent works demonstrated the usefulness of temporal coherence to regularize supervised training or to learn invariant features with deep architectures.

General Classification

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