3 citations · 4 across the 21 of their papers we have counts for
8 papers · 1 filter
Closed-form merging of parameter-efficient modules for Federated Continual Learning
Riccardo Salami, Pietro Buzzega, Matteo Mosconi +3
Model merging has emerged as a crucial technique in Deep Learning, enabling the integration of multiple models into a unified system while preserving perfor-mance and scalability.…
May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
Monica Millunzi, Lorenzo Bonicelli, Angelo Porrello +3
Forgetting presents a significant challenge during incremental training, making it particularly demanding for contemporary AI systems to assimilate new knowledge in streaming data…
An Attention-based Representation Distillation Baseline for Multi-Label Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini +3
The field of Continual Learning (CL) has inspired numerous researchers over the years, leading to increasingly advanced countermeasures to the issue of catastrophic forgetting. Mos…
CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning
Emanuele Frascaroli, Aniello Panariello, Pietro Buzzega +3
With the emergence of Transformers and Vision-Language Models (VLMs) such as CLIP, fine-tuning large pre-trained models has recently become a prevalent strategy in Continual Learni…
A Second-Order Perspective on Model Compositionality and Incremental Learning
Angelo Porrello, Lorenzo Bonicelli, Pietro Buzzega +3
The fine-tuning of deep pre-trained models has revealed compositional properties, with multiple specialized modules that can be arbitrarily composed into a single, multi-task model…
Selective Attention-based Modulation for Continual Learning
Giovanni Bellitto, Federica Proietto Salanitri, Matteo Pennisi +5
We present SAM, a biologically-plausible selective attention-driven modulation approach to enhance classification models in a continual learning setting. Inspired by neurophysiolog…
Semantic Residual Prompts for Continual Learning
Martin Menabue, Emanuele Frascaroli, Matteo Boschini +4
Prompt-tuning methods for Continual Learning (CL) freeze a large pre-trained model and train a few parameter vectors termed prompts. Most of these methods organize these vectors in…
Self-Labeling the Job Shop Scheduling Problem
Andrea Corsini, Angelo Porrello, Simone Calderara +1
This work proposes a self-supervised training strategy designed for combinatorial problems. An obstacle in applying supervised paradigms to such problems is the need for costly tar…