Skip to content

MIC: Multi-view Image Classifier using Generative Adversarial Networks for Missing Data Imputation

In this paper, we propose a framework for image classification tasks, named MIC, that takes as input multi-view images, such as RGB-T images for surveillance purposes. We combine auto-encoder and generative adversarial network architectures to ensure the multi-view embedding in a common latent space. Then, the resulting features are fed to the classification stage. The proposed framework is able to, all at once, train the multi-view embedding model to find a shared latent representation for the different views, perform data imputation (generate the missing views) and ensure the classification task by predicting the labels. Experiments on the MNIST dataset with a panoply of classifiers and several missingness ratios show the effectiveness of our solution.

Gianmarco Aversano, Mahmoud Jarraya, Maher Marwani, Ichraf Lahouli and Sabri Skhiri, MIC: Multi-view Image Classifier using Generative Adversarial Networks for Missing Data Imputation,  Proc. of the 18th IEEE International Multi-conference on Systems, Signals and Devices, 2021

Click here to access the paper.

Releated Posts

IEEE Big Data 2023 – A Summary

Our CTO, Sabri Skhiri, recently travelled to Sorrento for IEEE Big Data 2023. In this article, Sabri explores for you the various keynotes and talks that took place during the
Read More

Robust ML Approach for Screening MET Drug Candidates in Combination with Immune Checkpoint Inhibitors

Present study highlights the significance of dataset size in ICI microbiota models and presents a methodology to enhance the performances of a multi-cohort-based ML approach.
Read More