10 papers
Rethinking Expert Training for Model Merging with Prompt Learning
Christos Georgakilas, Aniello Panariello, Samir El Karrat Moreno +3
Model merging aims to combine multiple domain-specialized experts trained from a shared foundation model into a single multi-task model. Existing approaches largely focus on improv…
Transporting Task Vectors across Different Architectures without Training
Filippo Rinaldi, Aniello Panariello, Giacomo Salici +2
Adapting large pre-trained models to downstream tasks often produces task-specific parameter updates that are expensive to relearn for every model variant. While recent work has sh…
Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
Carlos Garrido-Munoz, Aniello Panariello, Silvia Cascianelli +4
Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods still require real samples fro…
Gradient-Sign Masking for Task Vector Transport Across Pre-Trained Models
Filippo Rinaldi, Aniello Panariello, Giacomo Salici +4
When a new release of a foundation model is published, practitioners typically need to repeat fine-tuning, even if the same task was already tackled in the previous version. A prom…
Accurate and Efficient Low-Rank Model Merging in Core Space
Aniello Panariello, Daniel Marczak, Simone Magistri +5
In this paper, we address the challenges associated with merging low-rank adaptations of large neural networks. With the rise of parameter-efficient adaptation techniques, such as…
Modular Embedding Recomposition for Incremental Learning
Aniello Panariello, Emanuele Frascaroli, Pietro Buzzega +3
The advent of pre-trained Vision-Language Models (VLMs) has significantly transformed Continual Learning (CL), mainly due to their zero-shot classification abilities. Such proficie…