Train a convolutional neural network to generate the contents of an arbitrary image region conditioned on its surroundings.

**LeVIT** is a hybrid neural network for fast inference image classification. LeViT is a stack of [transformer blocks](https://paperswithcode.com/method/transformer), with [pooling steps](https://paperswithcode.com/methods/category/pooling-operation) to reduce the resolution of the activation maps as in classical [convolutional architectures](https://paperswithcode.com/methods/category/convolutional-neural-networks). This replaces the uniform structure of a Transformer by a pyramid with pooling, similar to the [LeNet](https://paperswithcode.com/method/lenet) architecture

LeVIT

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

Inpainting

Context Encoders: Feature Learning by Inpainting

**Linformer** is a linear [Transformer](https://paperswithcode.com/method/transformer) that utilises a linear self-attention mechanism to tackle the self-attention bottleneck with [Transformer models](https://paperswithcode.com/methods/category/transformers). The original [scaled dot-product attention](https://paperswithcode.com/method/scaled) is decomposed into multiple smaller attentions through linear projections, such that the combination of these operations forms a low-rank factorization of the original attention.

Source	Context Encoders: Feature Learning by Inpainting
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com