**StyleGAN2** is a generative adversarial network that builds on [StyleGAN](https://paperswithcode.com/method/stylegan) with several improvements. First, [adaptive instance normalization](https://paperswithcode.com/method/adaptive-instance-normalization) is redesigned and replaced with a normalization technique called [weight demodulation](https://paperswithcode.com/method/weight-demodulation). Secondly, an improved training scheme upon progressively growing is introduced, which achieves the same goal - training starts by focusing on low-resolution images and then progressively shifts focus to higher and higher resolutions - without changing the network topology during training. Additionally, new types of regularization like lazy regularization and [path length regularization](https://paperswithcode.com/method/path-length-regularization) are proposed.

**Fast Sample Re-Weighting**, or **FSR**, is a sample re-weighting strategy to tackle problems such as dataset biases, noisy labels and imbalanced classes. It leverages a dictionary (essentially an extra buffer) to monitor the training history reflected by the model updates during meta optimization periodically, and utilises a valuation function to discover meaningful samples from training data as the proxy of reward data. The unbiased dictionary keeps being updated and provides reward signals to optimize sample weights. Additionally, instead of maintaining model states for both model and sample weight updates separately, feature sharing is enabled for saving the computation cost used for maintaining respective states.

Fast Sample Re-Weighting

Learning Fast Sample Re-weighting Without Reward Data

StyleGAN2

Analyzing and Improving the Image Quality of StyleGAN

Random Ensemble Mixture (REM) is an easy to implement extension of [DQN](https://paperswithcode.com/method/dqn) inspired by [Dropout](https://paperswithcode.com/method/dropout). The key intuition behind REM is that if one has access to multiple estimates of Q-values, then a weighted combination of the Q-value estimates is also an estimate for Q-values. Accordingly, in each training step, REM randomly combines multiple Q-value estimates and uses this random combination for robust training.

Source	Analyzing and Improving the Image Quality of StyleGAN
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com