An infinite image generator which is based on a patch-wise, periodically equivariant generator.

**SpatialDropout** is a type of [dropout](https://paperswithcode.com/method/dropout) for convolutional networks. For a given [convolution](https://paperswithcode.com/method/convolution) feature tensor of size $n\_{\text{feats}}$×height×width, we perform only $n\_{\text{feats}}$ dropout
trials and extend the dropout value across the entire feature map. Therefore, adjacent pixels in the dropped-out feature
map are either all 0 (dropped-out) or all active as illustrated in the figure to the right.

SpatialDropout

Efficient Object Localization Using Convolutional Networks

ALIS

Aligning Latent and Image Spaces to Connect the Unconnectable

The **Universal Transformer** is a generalization of the [Transformer](https://paperswithcode.com/method/transformer) architecture. Universal Transformers combine the parallelizability and global receptive field of feed-forward sequence models like the Transformer with the recurrent inductive bias of [RNNs](https://paperswithcode.com/methods/category/recurrent-neural-networks). They also utilise a dynamic per-position halting mechanism.

Source	Aligning Latent and Image Spaces to Connect the Unconnectable
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com