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Found 4 papers, 0 papers with code
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion.
Neural Volume Super-Resolution
This allows us to super-resolve the 3D scene representation by applying 2D convolutional networks on the 2D feature planes.
Light Field Implicit Representation for Flexible Resolution Reconstruction
consists of a feature extractor and a decoder which are trained on a dataset of light field patches.
Energy Dissipation with Plug-and-Play Priors
In this paper we consider the combination of both approaches by projecting the outputs of a plug-and-play denoising network onto the cone of descent directions to a given energy.
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