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SinIR: Efficient General Image Manipulation with Single Image Reconstruction
[article]
2021
arXiv
pre-print
We propose SinIR, an efficient reconstruction-based framework trained on a single natural image for general image manipulation, including super-resolution, editing, harmonization, paint-to-image, photo-realistic style transfer, and artistic style transfer. We train our model on a single image with cascaded multi-scale learning, where each network at each scale is responsible for image reconstruction. This reconstruction objective greatly reduces the complexity and running time of training,
arXiv:2106.07140v1
fatcat:qgkzy7c6vfhndei5r2ige77iru