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• W3097754836 abstract "A regular convolution layer applying a filter in the same way over known and unknown areas causes visual artifacts in the inpainted image. Several studies address this issue with feature re-normalization on the output of the convolution. However, these models use a significant amount of learnable parameters for feature re-normalization, or assume a binary representation of the certainty of an output. We propose (layer-wise) feature imputation of the missing input values to a convolution. In contrast to learned feature re-normalization, our method is efficient and introduces a minimal number of parameters. Furthermore, we propose a revised gradient penalty for image inpainting, and a novel GAN architecture trained exclusively on adversarial loss. Our quantitative evaluation on the FDF dataset reflects that our revised gradient penalty and alternative convolution improves generated image quality significantly. We present comparisons on CelebA-HQ and Places2 to current state-of-the-art to validate our model." @default.
• W3097754836 created "2020-11-09" @default.
• W3097754836 creator A5002687932 @default.
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• W3097754836 date "2020-11-02" @default.
• W3097754836 modified "2023-09-23" @default.
• W3097754836 title "Image Inpainting with Learnable Feature Imputation" @default.
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• W3097754836 hasPublicationYear "2020" @default.
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