8 papers
Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging
Salvatore Calcagno, Marco Finocchiaro, Giovanni Bellitto +3
Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experimental conditions, annotations ar…
TrustErase: Auditable Instant Machine Unlearning with Passport-Embedded Representations
Rutger Hendrix, Leonardo G. Russo, Concetto Spampinato +2
The demand for privacy-compliant AI has amplified the need for machine unlearning; yet, existing retraining or distillation-based methods remain unverifiable and computationally co…
Routing without Forgetting
Alessio Masano, Giovanni Bellitto, Dipam Goswani +2
Continual learning in transformers is commonly addressed through parameter-efficient adaptation: prompts, adapters, or LoRA modules are specialized per task while the backbone rema…
Dream2Learn: Structured Generative Dreaming for Continual Learning
Salvatore Calcagno, Matteo Pennisi, Federica Proietto Salanitri +4
Continual learning requires balancing plasticity and stability while mitigating catastrophic forgetting. Inspired by human dreaming as a mechanism for internal simulation and knowl…
DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe +5
Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework th…
Zero-Shot Decentralized Federated Learning
Alessio Masano, Matteo Pennisi, Federica Proietto Salanitri +2
CLIP has revolutionized zero-shot learning by enabling task generalization without fine-tuning. While prompting techniques like CoOp and CoCoOp enhance CLIP's adaptability, their e…