5 citations · 14 across the 20 of their papers we have counts for
22 papers
An Efficient Machine Learning Approach for Degradation Forecasting in AEM Water Electrolysis
Marco Veneriano, Ani Gjergji, Sebastiano Bellani +3
This study provides a data-driven analysis of a novel dataset of single-cell Anion Exchange Membrane water electrolyzers (AEMWE), operated under constant current load across multip…
Revisiting Diffusion Fine-Tuning for Unsupervised Domain Adaptation
Xuan Qi, Yi Wei, Daniele Berardini +2
Diffusion-based unsupervised domain adaptation (UDA) improves cross-domain transfer by generating target-specific synthetic data for downstream adaptation. Existing methods are lar…
TReViS: Temporal Repetition Structure Aware Video Synthesis for Self-supervised Repetitive Action Counting
Fanqi Yu, Shengming Ma, Stefano Fiorini +4
Fully supervised repetitive action counting (RAC) has achieved strong performance, but requires dense temporal annotations that are costly and difficult to scale. We propose TReViS…
CutClean: Neural Network Pruning for Privacy-Preserving Inference
Leonardo Magliolo, Vito Paolo Pastore, Giuseppe Valenzise +1
Neural networks are increasingly deployed in high-stakes applications with growing privacy leakage concerns. We show that this privacy leakage can occur even in the absence of repr…
VT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation
Xuan Qi, Daniele Berardini, Dario Serez +2
Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shift. Recent diffusion-based UD…
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
Daniele Berardini, Vito Paolo Pastore, Vittorio Murino
One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data d…
CDPM-Align: Multi-Scale Guidance-Aligned Diffusion Pretraining for Robust Few-Shot Anatomical Landmark Detection
Roberto Di Via, Irina Voiculescu, Francesca Odone +1
Anatomical landmark detection is a fundamental task in medical image analysis supporting a wide range of diagnostic and interventional workflows. Although recent methods have achie…
Infinite dimensional generative sensing
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Deep generative models have become a standard for modeling priors for inverse problems, going beyond classical sparsity-based methods. However, existing theoretical guarantees are…
Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
Ivan Luiz De Moura Matos, Abdel Djalil Sad Saoud, Ekaterina Iakovleva +2
The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulati…
Automated Landmark Detection for assessing hip conditions: A Cross-Modality Validation of MRI versus X-ray
Roberto Di Via, Vito Paolo Pastore, Francesca Odone +2
Many clinical screening decisions are based on angle measurements. In particular, FemoroAcetabular Impingement (FAI) screening relies on angles traditionally measured on X-rays. Ho…
How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Nathan Roos, Ekaterina Iakovleva, Ani Gjergji +2
Diffusion-based generative models demonstrate state-of-the-art performance across various image synthesis tasks, yet their tendency to replicate and amplify dataset biases remains…
Disentangled representations of microscopy images
Jacopo Dapueto, Vito Paolo Pastore, Nicoletta Noceti +1
Microscopy image analysis is fundamental for different applications, from diagnosis to synthetic engineering and environmental monitoring. Modern acquisition systems have granted t…
DIMA: DIffusing Motion Artifacts for unsupervised correction in brain MRI images
Paolo Angella, Luca Balbi, Fabrizio Ferrando +4
Motion artifacts remain a significant challenge in Magnetic Resonance Imaging (MRI), compromising diagnostic quality and potentially leading to misdiagnosis or repeated scans. Exis…
Assessing the use of Diffusion models for motion artifact correction in brain MRI
Paolo Angella, Vito Paolo Pastore, Matteo Santacesaria
Magnetic Resonance Imaging generally requires long exposure times, while being sensitive to patient motion, resulting in artifacts in the acquired images, which may hinder their di…
Diffusing DeBias: Synthetic Bias Amplification for Model Debiasing
Massimiliano Ciranni, Vito Paolo Pastore, Roberto Di Via +3
Deep learning model effectiveness in classification tasks is often challenged by the quality and quantity of training data whenever they are affected by strong spurious correlation…
Say My Name: a Model's Bias Discovery Framework
Massimiliano Ciranni, Luca Molinaro, Carlo Alberto Barbano +4
In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases t…
Self-supervised pre-training with diffusion model for few-shot landmark detection in x-ray images
Roberto Di Via, Francesca Odone, Vito Paolo Pastore
Deep neural networks have been extensively applied in the medical domain for various tasks, including image classification, segmentation, and landmark detection. However, their app…
Looking at Model Debiasing through the Lens of Anomaly Detection
Vito Paolo Pastore, Massimiliano Ciranni, Davide Marinelli +2
It is widely recognized that deep neural networks are sensitive to bias in the data. This means that during training these models are likely to learn spurious correlations between…
Is in-domain data beneficial in transfer learning for landmarks detection in x-ray images?
Roberto Di Via, Matteo Santacesaria, Francesca Odone +1
In recent years, deep learning has emerged as a promising technique for medical image analysis. However, this application domain is likely to suffer from a limited availability of…
Efficient Unsupervised Learning for Plankton Images
Paolo Didier Alfano, Marco Rando, Marco Letizia +3
Monitoring plankton populations in situ is fundamental to preserve the aquatic ecosystem. Plankton microorganisms are in fact susceptible of minor environmental perturbations, that…
Top-Tuning: a study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods
Paolo Didier Alfano, Vito Paolo Pastore, Lorenzo Rosasco +1
The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and infe…
Embedded System to Detect, Track and Classify Plankton Using a Lensless Video Microscope
Thomas G. Zimmerman, Vito P. Pastore, Sujoy K. Biswas +1
Plankton provide the foundation for life on earth. To advance our understanding of the marine ecosystem, for scientific, commercial and survival purposes, more in situ continuous m…