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.

**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.

DeepSIM

Image Shape Manipulation from a Single Augmented Training Sample

Local Relation Layer

Local Relation Networks for Image Recognition

**DeepWalk** learns embeddings (social representations) of a graph's vertices, by modeling a stream of short random walks. Social representations are latent features of the vertices that capture neighborhood similarity and community membership. These latent representations encode social relations in a continuous vector space with a relatively small number of dimensions. It generalizes neural language models to process a special language composed of a set of randomly-generated walks. 

The goal is to learn a latent representation, not only a probability distribution of node co-occurrences, and so as to introduce a mapping function $\Phi \colon v \in V \mapsto \mathbb{R}^{|V|\times d}$.
This mapping $\Phi$ represents the latent social representation associated with each vertex $v$ in the graph. In practice, $\Phi$ is represented by a $|V| \times d$ matrix of free parameters.

Source	Local Relation Networks for Image Recognition
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com