Author

Vito Paolo Pastore

2 papers indexed here

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Preprint Sep 2026

Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation

Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are largely designed for single-target adaptation: when a model trained on one labeled source domain must be adapted to multiple unlabeled target d...

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