Deep Layer Aggregation Architectures for Photorealistic Universal Style Transfer

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...

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Bibliographic Details
Main Authors: Marius Dediu, Costin-Emanuel Vasile, Călin Bîră
Format: Article
Language:English
Published: MDPI AG 2023-05-01
Series:Sensors
Subjects:
deep learning
photorealistic
style transfer
deep layer aggregation
Online Access:https://www.mdpi.com/1424-8220/23/9/4528

Internet

https://www.mdpi.com/1424-8220/23/9/4528

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