Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Progressive GAN (PROGAN) <s> Learning to solve complex sequences of tasks--while both leveraging transfer and avoiding catastrophic forgetting--remains a key obstacle to achieving human-level intelligence. The progressive networks approach represents a step forward in this direction: they are immune to forgetting and can leverage prior knowledge via lateral connections to previously learned features. We evaluate this architecture extensively on a wide variety of reinforcement learning tasks (Atari and 3D maze games), and show that it outperforms common baselines based on pretraining and finetuning. Using a novel sensitivity measure, we demonstrate that transfer occurs at both low-level sensory and high-level control layers of the learned policy. <s> BIB001 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Progressive GAN (PROGAN) <s> We describe a new training methodology for generative adversarial networks. The key idea is to grow both the generator and discriminator progressively: starting from a low resolution, we add new layers that model increasingly fine details as training progresses. This both speeds the training up and greatly stabilizes it, allowing us to produce images of unprecedented quality, e.g., CelebA images at 1024^2. We also propose a simple way to increase the variation in generated images, and achieve a record inception score of 8.80 in unsupervised CIFAR10. Additionally, we describe several implementation details that are important for discouraging unhealthy competition between the generator and discriminator. Finally, we suggest a new metric for evaluating GAN results, both in terms of image quality and variation. As an additional contribution, we construct a higher-quality version of the CelebA dataset. <s> BIB002 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Progressive GAN (PROGAN) <s> Despite recent progress in generative image modeling, successfully generating high-resolution, diverse samples from complex datasets such as ImageNet remains an elusive goal. To this end, we train Generative Adversarial Networks at the largest scale yet attempted, and study the instabilities specific to such scale. We find that applying orthogonal regularization to the generator renders it amenable to a simple "truncation trick," allowing fine control over the trade-off between sample fidelity and variety by reducing the variance of the Generator's input. Our modifications lead to models which set the new state of the art in class-conditional image synthesis. When trained on ImageNet at 128x128 resolution, our models (BigGANs) achieve an Inception Score (IS) of 166.5 and Frechet Inception Distance (FID) of 7.4, improving over the previous best IS of 52.52 and FID of 18.6. <s> BIB003 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Progressive GAN (PROGAN) <s> We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature. The new architecture leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images (e.g., freckles, hair), and it enables intuitive, scale-specific control of the synthesis. The new generator improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. To quantify interpolation quality and disentanglement, we propose two new, automated methods that are applicable to any generator architecture. Finally, we introduce a new, highly varied and high-quality dataset of human faces. <s> BIB004
PROGAN involves progressive steps toward the expansion of the network architecture BIB002 . This architecture uses the idea of progressive neural networks first proposed in BIB001 . This technology does not suffer from forgetting and can leverage prior knowledge via lateral connections to previously learned features. Consequently it is widely applied for learning complex task sequences. Figure. 6 demonstrates the training process for PROGAN. Training starts with low resolution 4 × 4 pixels image. Both G and D start to grow with the training progressing. Importantly, all variables remain trainable throughout this growing process. This progressive training strategy enables substantially more stable learning for both networks. By increasing the resolution little by little, the networks are continuously asked a much simpler question comparing to the end goal of discovering a mapping from latent vectors. All current state-of-the-art GANs employ this type of training strategy and it has resulted in impressive, plausible images BIB004 , BIB002 , BIB003 . 