Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer

This paper introduces a deep learning approach to photorealistic universal style transfer that extends the PhotoNet network architecture by adding extra feature-aggregation modules. Given a pair of images representing the content and the reference of style, we augment the state-of-the-art solution m...

Descripción completa

Detalles Bibliográficos
Autores principales: Dediu, Marius, Vasile, Costin-Emanuel, Bîră, Călin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181698/
https://www.ncbi.nlm.nih.gov/pubmed/37177731
http://dx.doi.org/10.3390/s23094528

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10181698/
https://www.ncbi.nlm.nih.gov/pubmed/37177731
http://dx.doi.org/10.3390/s23094528

Ejemplares similares