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• W3120472021 abstract "Recently, there is a vast interest in developing image feature learning methods that are independent of the training data, such as deep image prior [35], InGAN [28], [29], SinGAN [27], and DCIL [8]. These methods perform various tasks, such as image restoration, image editing, and image synthesis. In this work, we proposed a new training data-independent framework, called Deep Contextual Features Learning (DeepCFL), to perform image synthesis and image restoration based on the semantics of the input image. The contextual features are simply the high dimensional vectors representing the semantics of the given image. DeepCFL is a single image GAN framework that learns the distribution of the context vectors from the input image. We show the performance of contextual learning in various challenging scenarios: outpainting, inpainting, and restoration of randomly removed pixels. DeepCFL is applicable when the input source image and the generated target image are not aligned. We illustrate image synthesis using DeepCFL for the task of image resizing." @default.
• W3120472021 created "2021-01-18" @default.
• W3120472021 creator A5020425550 @default.
• W3120472021 creator A5070142969 @default.
• W3120472021 date "2021-01-01" @default.
• W3120472021 modified "2023-09-23" @default.
• W3120472021 title "DeepCFL: Deep Contextual Features Learning from a Single Image" @default.
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• W3120472021 doi "https://doi.org/10.1109/wacv48630.2021.00294" @default.
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