The algorithm for the Transformer-DN model.

<div><p>This study addresses the limitations of traditional interior space design, particularly the timeliness and uniqueness of solutions, by proposing an optimized design framework that integrates a two-stage deep learning network with a single-sample-driven mechanism. In the first sta...

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Bibliographic Details
Main Author:	Liang Na (22226855) (author)
Other Authors:	Zhou Hui (22226858) (author), Xia Huaxia (22226861) (author)
Published:	2025
Subjects:	Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified nyu depth v2 new technological paradigm interior space based input space images experimental results demonstrate model &# 8217 interior space design initial feature vector design results based color matching scores optimized design framework stage network enhances transformer network optimization design framework employs feature extraction diffusion model design efficiency design cycle color distribution second stage first stage xlink "> traditional methods synergistic effect sun rgb spatial layout significant improvement scale coordination iteratively optimize including interiornet generative optimization furniture style extract multi etc .) driven mechanism dimensional features artificial intelligence also presents 25 %, 20 %.
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The algorithm for the Transformer-DN model.

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