GANs are prone to the escalation of signal magnitudes as a result of unhealthy competition between the two networks. Most if not all earlier solutions discourage this by using a variant of batch normalization (Ioffe & Szegedy, 2015; Salimans & Kingma, 2016; Ba et al., 2016) in the generat...
from Nvidia titled “Progressive Growing of GANs for Improved Quality, Stability, and Variation.” The key innovation of the Progressive Growing GAN is the incremental increase in the size of images output by the generator, starting with a 4×4 pixel image and doubling to 8×8, 16×16, and...
Our primary contribution is a training methodology for GANs where we start with low-resolution images, and then progressively increase the resolution by adding layers to the networks as visualized in Figure 1. This incremental nature allows the training to first discover large-scale structure of the...
, 2021). To conclude, all these methods focus on the image domain, while audio data is different for its long-term dependencies and strong condition. Liu et al. (2022) proposes a denoising diffusion generative adversarial networks (GANs) to achieve high-fidelity and efficient text-to-...
Hehlgans, R.E. Mebius, M. van der Wall, D. Kruspe, C. Englert, A. Lovas, D. Hu, G.J. Randolph, F. Weih, A.J. Habenicht Lymphotoxin β receptor signaling promotes tertiary lymphoid organogenesis in the aorta adventitia of aged ApoE−/− mice J. Exp. Med., 206 (2009), ...
and the network structure is constantly adjusted. The main advantage of this approach is that most of the iterations of the PGGAN are performed at lower resolutions, which increases the training speed by a factor of 2–6 compared to that of the traditional GANs. The PGGAN first creates the...