mql5

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.

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