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...
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-...
Embodiments also use generative adversarial networks (GANs) for training a machine-learning based denoiser as an alternative to using pre-defined loss functions. The training may involve simultaneously optimizing two models: a generator or denoiser that captures data distribution, and a discriminator that...