ImpRoving Conditional Generative Adversarial Networks for Inverse Design of Plasmonic Nanostructures Using Label Projection and Embedding Methods

We introduce label projection and embedding methods to improve generative adversarial networks. As proof of concept, we generate images of plasmonic nanostructures with different shapes by using a model trained on their linear optical response.

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Published
2025-06-23
DOI
https://doi.org/10.1109/cleo/europe-eqec65582.2025.11110418
Citations
1
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
1.28
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article

ImpRoving Conditional Generative Adversarial Networks for Inverse Design of Plasmonic Nanostructures Using Label Projection and Embedding Methods

Nicolò Maccaferri, Nils Henriksson, P. A. Persson
1 citations
Image Enhancement Techniques
1.28
article

ImpRoving Conditional Generative Adversarial Networks for Inverse Design of Plasmonic Nanostructures Using Label Projection and Embedding Methods

Nicolò Maccaferri, Nils Henriksson, P. A. Persson
article en
1 citations

Abstract

We introduce label projection and embedding methods to improve generative adversarial networks. As proof of concept, we generate images of plasmonic nanostructures with different shapes by using a model trained on their linear optical response.

Umeå University (SE)
Openalex Percentile: Top 16%
Image Enhancement Techniques
1.28
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ImpRoving Conditional Generative Adversarial Networks for Inverse Design of Plasmonic Nanostructures Using Label Projection and Embedding Methods — Nicolò Maccaferri, Nils Henriksson, et al. · (2025) | TGRS Research Map | TGRS