<![CDATA[Generative Adversarial Networks (GANs) are a type of neural network that can generate synthetic images that are often indistinguishable from real ones. The article explores GAN to augment existing datasets or generate new ones for training classifiers. The competitive training process of GANs results in a generator network that can produce increasingly realistic images to create more diverse and balanced datasets for training classifiers. The article discusses several successful applications of GANs in image classification, including object recognition, face classification, and medical image analysis. The datasets used in this article are CelebA and FER2013. The CelebA dataset consists of 202,599 celebrity images with 40 attributes, such as gender, age, and facial hair. The FER2013 dataset consists of 35,887 images of faces with seven other emotions, including anger, disgust, fear, happiness, sadness, surprise, and neutral. The dataset is divided into training, validation, and test sets. We resized the images to 64x64 pixels and normalized the pixel values between -1 and 1, then trained a GAN model using the dataset. We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). We evaluate the performance of our approach and compare it with several state-of-the-art methods, including Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), with the results that our approach outperforms SVM and CNN methods on both datasets, achieving a classification accuracy of 89.2% on CelebA and 72.5% in FER2013. Meanwhile, classification accuracy on SVM was 82.3% on CelebA and 65.4% on FER2013. Classification accuracy on CNN is 87.9% on CelebA and 70.8% on FER2013.]]>
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