Author

Vito Paolo Pastore

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Preprint Sep 2026

Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation

MUSE (Multi-target UDA-oriented Synthesis with Efficient diffusion fine-tuning), a decoupled adaptation framework that separates source-supervised semantic adaptation from target-specific style adaptation, enabling target-specific generation while avoiding repeated source-guided fine-tuning for each target.

Qi Xuan, Yi-Xiang Wei, Daniele Berardini et al. · 0 citations

Conference Open access Aug 2026

CutClean: Neural Network Pruning for Privacy-Preserving Inference

CutClean is a privacy-aware pruning method that allows to reduce privacy information flow through the network, while increasing its sparsity, and effectively minimizes private information flow while achieving high sparsity rates and preserving classification target accuracy.

Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise et al. · International Conference on... · 0 citations