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• W2949811693 abstract "We present a Deep Convolutional Neural Network architecture which serves as a generic image-to-image regressor that can be trained end-to-end without any further machinery. Our proposed architecture: the Recursively Branched Deconvolutional Network (RBDN) develops a cheap multi-context image representation very early on using an efficient recursive branching scheme with extensive parameter sharing and learnable upsampling. This multi-context representation is subjected to a highly non-linear locality preserving transformation by the remainder of our network comprising of a series of convolutions/deconvolutions without any spatial downsampling. The RBDN architecture is fully convolutional and can handle variable sized images during inference. We provide qualitative/quantitative results on $3$ diverse tasks: relighting, denoising and colorization and show that our proposed RBDN architecture obtains comparable results to the state-of-the-art on each of these tasks when used off-the-shelf without any post processing or task-specific architectural modifications." @default.
• W2949811693 created "2019-06-27" @default.
• W2949811693 creator A5029980724 @default.
• W2949811693 creator A5038267072 @default.
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• W2949811693 date "2016-12-10" @default.
• W2949811693 modified "2023-09-27" @default.
• W2949811693 title "Generalized Deep Image to Image Regression" @default.
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• W2949811693 hasPublicationYear "2016" @default.
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