PA-GAN: Improving GAN Training by Progressive Augmentation

By Dan Zhang, Anna Khoreva. 2018

24 citations recorded by the source corpus.

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In Generative Models

Citations in the corpus

Training Generative Adversarial Networks with Limited Data (2020, methodology): “The key problem with small datasets is that the discriminator overfits to the training examples; its feedback to the generator becomes meaningless and training starts to diverge [2, 48].”
A U-Net Based Discriminator for Generative Adversarial Networks (2020, background): “Plenty of efforts have been made to improve the training and performance of GANs, from reformulation of the objective function [31, 1, 26, 37], integration of different regularization techniques [51, 34, 40, 48] and architectural changes [38, 19, 13, 27].”
Enhancing Photorealism Enhancement (2021, background): “Thus high-capacity networks may overfit to spurious artifacts in the data, leading to suboptimal performance of the generator [76], [77].”
Data-Efficient Instance Generation from Instance Discrimination (2021, methodology): “Some recent attempts [24, 39, 49, 51, 44] have been made to apply data augmentation to GAN training.”
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis (2021, background): “We use LPIPS to report the reconstruction quality when we perform latent space back-tracking on G given real images, and measure the auto-encoding performance.”
You Only Need Adversarial Supervision for Semantic Image Synthesis (2020, background): “Despite the recent progress on stabilizing GANs (Gulrajani et al., 2017; Miyato et al., 2018; Zhang & Khoreva, 2019) and developing their architectures (Zhang et al., 2019; Karras et al., 2019), state-of-the-art GAN-based semantic image synthesis models (Park et al., 2019; Liu et al., 2019) still…”