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Found 8 papers, 2 papers with code
Self-supervised Depth Denoising Using Lower- and Higher-quality RGB-D sensors
Consumer-level depth cameras and depth sensors embedded in mobile devices enable numerous applications, such as AR games and face identification.
Precipitation Nowcasting with Satellite Imagery
Precipitation nowcasting is a short-range forecast of rain/snow (up to 2 hours), often displayed on top of the geographical map by the weather service.
Spatiotemporal Data Fusion for Precipitation Nowcasting
Precipitation nowcasting using neural networks and ground-based radars has become one of the key components of modern weather prediction services, but it is limited to the regions covered by ground-based radars.
Impostor Networks for Fast Fine-Grained Recognition
In this work we introduce impostor networks, an architecture that allows to perform fine-grained recognition with high accuracy and using a light-weight convolutional network, making it particularly suitable for fine-grained applications on low-power and non-GPU enabled platforms.
Learnable Visual Markers
We propose a new approach to designing visual markers (analogous to QR-codes, markers for augmented reality, and robotic fiducial tags) based on the advances in deep generative networks.
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example.
Fast ConvNets Using Group-wise Brain Damage
We revisit the idea of brain damage, i. e. the pruning of the coefficients of a neural network, and suggest how brain damage can be modified and used to speedup convolutional layers.
Speeding-up Convolutional Neural Networks Using Fine-tuned CP-Decomposition
We propose a simple two-step approach for speeding up convolution layers within large convolutional neural networks based on tensor decomposition and discriminative fine-tuning.
