Dynamic Sparse Training method based on using the gradient norm to select which weights to remove/add in each mask update.

**StyleALAE** is a type of [adversarial latent autoencoder](https://paperswithcode.com/method/alae) that uses a [StyleGAN](https://paperswithcode.com/method/stylegan) based generator. For this the latent space $\mathcal{W}$ plays the same role as the intermediate latent space in [StyleGAN](https://paperswithcode.com/method/stylegan). Therefore, the $G$ network becomes the part of StyleGAN depicted on the right side of the Figure. The left side is a
novel architecture that we designed to be the encoder $E$. The StyleALAE encoder has [Instance Normalization](https://paperswithcode.com/method/instance-normalization) (IN) layers to extract multiscale style information that is combined into a latent code $w$ via a learnable multilinear map.

StyleALAE

Adversarial Latent Autoencoders

RigL

Rigging the Lottery: Making All Tickets Winners

A method that randomly mask out all features coming from a specific time-step in time-series data. If the model used is independent of uneven sequences or missing data in time-series, like attention-based transformers, the masked time-steps can be just ignored in the forward prediction of the model. Otherwise, they have to be masked out with some numerical value.

Source	Rigging the Lottery: Making All Tickets Winners
Year
Data Source	Papers with Code archive (CC BY-SA)