**DeepSIM** is a generative model for conditional image manipulation based on a single image. The network learns to map between a primitive representation of the image to the image itself. At manipulation time, the generator allows for making complex image changes by modifying the primitive input representation and mapping it through the network. The choice of a primitive representations has an impact on the ease and expressiveness of the manipulations and can be automatic (e.g. edges), manual, or hybrid such as edges on top of segmentations.

**DiCENet** is a convolutional neural network architecture that utilizes dimensional convolutions (and dimension-wise fusion). The dimension-wise convolutions apply light-weight convolutional filtering across each dimension of the input tensor while dimension-wise fusion efficiently combines these dimension-wise representations; allowing the [DiCE Unit](https://paperswithcode.com/method/dice-unit) in the network to efficiently encode spatial and channel-wise information contained in the input tensor.

DiCENet

DiCENet: Dimension-wise Convolutions for Efficient Networks

DeepSIM

Image Shape Manipulation from a Single Augmented Training Sample

A **Local Relation Layer** is an image feature extractor that is an alternative to a [convolution](https://paperswithcode.com/method/convolution) operator. The intuition is that aggregation in convolution is basically a pattern matching process that applies fixed filters, which can be inefficient at modeling visual elements with varying spatial distributions. The local relation layer adaptively determines aggregation weights based on the compositional relationship of local pixel pairs. It is argued that, with this relational approach, it can composite visual elements into higher-level entities in a more efficient manner that benefits semantic inference.

Source	Image Shape Manipulation from a Single Augmented Training Sample
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com