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Semantic Image Inpainting with Deep Generative Models
TLDR
We propose a novel method for semantic image inpainting, which generates the missing content by conditioning on the available data. Expand
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Learning to See in the Dark
TLDR
We propose a new image processing pipeline that addresses the challenges of extreme low-light photography via a data-driven approach, based on end-to-end training of a fully-convolutional network. Expand
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Semantic Image Inpainting with Perceptual and Contextual Losses
TLDR
We propose a novel method for image inpainting based on a Deep Convolutional Generative Adversarial Network (DCGAN). Expand
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Seeing Motion in the Dark
TLDR
We present deep processing of very dark raw videos: on the order of one lux of illuminance. Expand
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Infrared and visible image fusion using total variation model
TLDR
In this paper, we propose a novel fusion strategy for infrared (IR) and visible images based on total variation (TV) minimization. Expand
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Image Restoration with Deep Generative Models
TLDR
We propose a unified framework for image restoration based on MAP and generative adversarial nets that can be applied to many image restoration problems. Expand
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Privacy preserving growing neural gas over arbitrarily partitioned data
TLDR
We present a privacy preserving protocol for the multiparty training of growing neural gas while the data are arbitrarily partitioned over different parties. Expand
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Learning with Forest Sparsity
TLDR
In this paper, we investigate a new model called \emph{forest sparsity} for sparse learning and compressive sensing. Expand
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NOMA for Energy-Efficient LiFi-Enabled Bidirectional IoT Communication
TLDR
In this paper, we consider a light fidelity (LiFi)-enabled bidirectional Internet of Things (IoT) communication system, where visible light and infrared light are used in the downlink and uplink, respectively. Expand
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