Generative adversarial network: An overview of theory and applications. Denoising diffusion probabilistic models. ![]() Hierarchical text-conditional image generation with clip latents. The difference between the architecture for the CIFAR-10 dataset is at the classifier, in which 256 filters are used in the last three ResBlocks. Each ResBlock is composed of two convolutional layers. The values in the brackets indicate the number of convolutional filters or nodes of the layers. Furthermore, since ScoreGAN does not use the Inception network as the evaluator and the score, it is difficult to regard the generated samples by ScoreGAN as adversarial examples of the Inception network, as shown in the examples in Figure 2, where the images are far from noises.Īrchitecture of the neural network modules for the training of the CIFAR-100 dataset. Therefore, this enhancement of the decreased FIDs could be evidence that ScoreGAN does not overfit on the Inception scores, and the proposed evaluator enhances the performance. Since the FID measures the similarity between feature distributions, it is less related to the objective of ScoreGAN. This is reflected not only in an increase in the Inception scores, but also in a decrease in the FID scores. It can be said that the generator in ScoreGAN appears to properly learn general features through the pretrained evaluator and is then enforced to produce a variety of samples by maximizing the score. The results of this study appear to validate the effectiveness of both the additional evaluator and auxiliary score present in ScoreGAN.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |