Dataset (GAN)

Le résumé fourni par la source

The Ekush dataset has been applied in our work which is publicly available at https://shahariarrabby.github.io/ekush/. There are two proposed methods- DCGAN(Deep Convolutional Generative Adversarial Network ) and Outlier. The outlier removed classes dataset has been given as named Dataset(outlier_removed).zip. For DCGAN, five classes have been used as there contains more imbalanced dataset. DCGAN_Generated_Images.zip contains the datasets of DCGAN generated images. Three datasets have been classified in this experiment using the ResNet-50 classifier. These three approaches datasets are split into three sets - train, validation, and test. First, the original dataset found in Dataset(original).zip has been used in the classifier. Then the outlier removed classes created in Dataset(outlier_removed).zip with the rest of the original dataset has used. Finally, together with DCGAN generate images and outlier removed classes created in Dataset(DCGAN_applied).zip with the other classes of the dataset used in the classifier.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.