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Style transfer aims to transfer arbitrary visual styles to content images. Weexplore algorithms adapted from two papers that try to solve the problem ofstyle transfer while generalizing on unseen styles or compromised visualquality. Majority of the improvements made focus on optimizing the algorithmfor real-time style transfer while adapting to new styles with considerablyless resources and constraints. We compare these strategies and compare howthey measure up to produce visually appealing images. We explore two approachesto style transfer: neural style transfer with improvements and universal styletransfer. We also make a comparison between the different images produced andhow they can be qualitatively measured.

A Comprehensive Comparison between Neural Style Transfer and Universal Style Transfer