The effect of data augmentation using infogan and RDA-GAN on convolutional neural networks

In the thesis study, the performance of CNN-based classification models under limited data conditions was examined using GAN-based data generation. Two different GAN algorithms, InfoGAN and RDA-GAN, were used in the research. Three different datasets, CIFAR-100, MNIST, and CelebAMask-HQ, were used for synthetic data generation in the data generation phase, and the best classification results were obtained from the MNIST dataset. The lowest FID value found in the data generated with InfoGAN on the MNIST data was 24.38361444380, and the classification results were very close to the original MNIST data. In classification using semi-synthetic data consisting of half original data, the DenseNet169 model achieved an F1-Score of 0.9213 and a test accuracy of 92.20%, which were very close to the DenseNet169 performance obtained with the original MNIST data F1-Score 0.9388, test accuracy 93.90%. In the classification performed only with synthetic data, the ResNet50 model showed an F1-Score of 0.7098 and a test accuracy of 70.30%, and these results showed a performance close to the ResNet50 performance obtained with the original MNIST data F1-Score 0.9377, test accuracy 93.80%. The best FID value observed in the data generated on the MNIST dataset using the RDA-GAN algorithm was 48.3179143859, and the classification results were very close to the original data. In classification using semi-synthetic data, the DenseNet169 model achieved an F1-Score of 0.9210 and a test accuracy of 92.10%, while classification using only generated data resulted in an F1-Score of 0.8454 and a test accuracy of 84.50%. The VGG16 model was also used in the classification studies, but the best performance was achieved by DenseNet169 and ResNet50. The findings show that GAN-based data generation can reflect the performance of CNN models quite closely to the original data under limited data conditions.