Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0

no code implementations • 13 Jul 2023 • Luigi Capogrosso, Alessio Mascolini, Federico Girella, Geri Skenderi, Sebastiano Gaiardelli, Nicola Dall'Ora, Francesco Ponzio, Enrico Fraccaroli, Santa Di Cataldo, Sara Vinco, Enrico Macii, Franco Fummi, Marco Cristani

Industry 4. 0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity.

Anomaly Detection Denoising

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

no code implementations • 16 Jul 2021 • Alessio Mascolini, Dario Cardamone, Francesco Ponzio, Santa Di Cataldo, Elisa Ficarra

We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines.

Representation Learning Self-Supervised Learning +1

W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

no code implementations • 24 Mar 2021 • Francesco Ponzio, Enrico Macii, Elisa Ficarra, Santa Di Cataldo

To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training, and a Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time.

Bayesian Inference Classification +2