| Title | Deep Learning Approach for Arbitrary Image Style Fusion and Transformation using SANET model |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Rathi, P., Adarsh, P., Kumar, M. |
| Conference Name | 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184) |
| Date Published | jun |
| Keywords | arbitrary image style fusion, arbitrary image stylization, arbitrary style transformation, arbitrary style-transformation procedures, ART, ASPM, Computational modeling, Computer vision, Conferences, content-image, convolutional neural networks, Deep Learning, generated artwork, identity-loss function, image fusion, image processing, impressive artworks, learning (artificial intelligence), machine learning, Market research, neural nets, neural style transfer, Optimization, Predictive Metrics, pubcrawl, real-time applications, real-time fusion, rendering (computer graphics), Resiliency, running time, SANET, SANET model, Scalability, semantic-structure, Shape, style characteristics, style-image |
| Abstract | For real-time applications of arbitrary style transformation, there is a trade-off between the quality of results and the running time of existing algorithms. Hence, it is required to maintain the equilibrium of the quality of generated artwork with the speed of execution. It's complicated for the present arbitrary style-transformation procedures to preserve the structure of content-image while blending with the design and pattern of style-image. This paper presents the implementation of a network using SANET models for generating impressive artworks. It is flexible in the fusion of new style characteristics while sustaining the semantic-structure of the content-image. The identity-loss function helps to minimize the overall loss and conserves the spatial-arrangement of content. The results demonstrate that this method is practically efficient, and therefore it can be employed for real-time fusion and transformation using arbitrary styles. |
| DOI | 10.1109/ICOEI48184.2020.9143024 |
| Citation Key | rathi_deep_2020 |
Deep Learning Approach for Arbitrary Image Style Fusion and Transformation using SANET model