Image generation using only a discriminator network with gradient norm penalty

Yeşilçimen, Cansu Cemre
This thesis explores the idea of generating images using only a discriminator network by extending a previously proposed method (Tapli, 2021) in several ways. The base method works by iteratively updating the input image, which is pure noise at the beginning while increasing the discriminator's score. We extend the training procedure of the base network by adding the following new losses: (i) total variation, (ii) N-way classification (if labels are available), and (iii) gradient norm penalty on real examples. Our experiments show that while the total variation and N-way classification do not significantly improve the performance, the gradient norm penalty results in better generative examples and faster convergence. Combining all three modifications yield the best model. Using a small convolutional network, we achieve an FID score of 25.26 on the MNIST dataset. We demonstrate additional generation results on the EMNIST and Yale Face datasets and present scores for out-of-distribution detection on FashionMNIST, EMNIST, and KMNIST datasets.


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In this thesis, we propose an image generation method that only involves a discriminator network; no generator or decoder networks are required. To generate an image, we iteratively apply an adversarial attack on the discriminator by updating the input image, which is noise at the beginning, to maximize the discriminator's output score. Generated images are then used as negative examples, together with the real images as positive examples, to fine-tune the discriminator. After several rounds of generation a...
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Citation Formats
C. C. Yeşilçimen, “Image generation using only a discriminator network with gradient norm penalty,” M.S. - Master of Science, Middle East Technical University, 2022.