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AMI-Class: Towards a Fully Automated Multi-view Image Classifier

In this paper, we propose an automated framework for multi-view image classification tasks. We combined a GAN-based multi-view embedding architecture with a scalable AutoML library, DeepHyper. The proposed framework is able to, all at once, train a model to find a common latent representation and perform data imputation, choose the best classifier and tune all necessary hyper-parameters. Experiments on the MNIST data-set show the effectiveness of our solution to optimize the end-to-end multi-view classification pipeline.

Mahmoud Jarraya, Maher Marwani, Gianmarco Aversano, Ichraf Lahouli and Sabri Skhiri, AMI-Class: Towards a Fully Automated Multi-view Image Classifier, In Proc. of The 19th International Conference on Computer Analysis of Images and Patterns CAIP2021, September 2021.

Click here to access the paper.

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