Discussing the article: "Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)"
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Check out the new article: Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN).
In this article, I will get acquainted with the GTGAN algorithm, which was introduced in January 2024 to solve complex problems of generation architectural layouts with graph constraints.
The initial state of the environment is most often analyzed using models that utilize convolutional layers or various attention mechanisms. However, convolutional architectures lack understanding of long-term dependencies in the original data due to inherent inductive biases. Architectures based on attention mechanisms allow the encoding of long-term or global relationships and the learning of highly expressive feature representations. On the other hand, graph convolution models make good use of local and neighboring vertex correlations based on the graph topology. Therefore, it makes sense to combine graph convolution networks and Transformers to model local and global interactions in an attempt to implement the search for optimal trading strategies.
The recently published paper "Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation" introduces the algorithm for the graph transformer generative adversarial model (GTGAN), which succinctly combines both of these approaches. The authors of the GTGAN algorithm address the problem of creating a realistic architectural design of a house from an input graph. The generator model they presented consists of three components: a message passing convolutional neural network (Conv-MPN), Graph Transformer encoder (GTE) and generation head.
Qualitative and quantitative experiments on three complex graphically constrained architectural layout generations with three datasets that were presented in the paper demonstrate that the proposed method can generate results superior to previously presented algorithms.
Author: Dmitriy Gizlyk