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Found 11 papers, 7 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.
Neural Point-Based Graphics
A deep rendering network is learned in parallel with the descriptors, so that new views of the scene can be obtained by passing the rasterizations of a point cloud from new viewpoints through this network.
Textured Neural Avatars
In particular, our system estimates an explicit two-dimensional texture map of the model surface.
Generative Models for Fast Calorimeter Simulation.LHCb case
Simulation is one of the key components in high energy physics.
Image Manipulation with Perceptual Discriminators
The new architecture, that we call a perceptual discriminator, embeds the convolutional parts of a pre-trained deep classification network inside the discriminator network.
Deep Image Prior
In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
Ranked #4 on
Feature Upsampling
on ImageNet
It Takes (Only) Two: Adversarial Generator-Encoder Networks
Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning.
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems.
Instance Normalization: The Missing Ingredient for Fast Stylization
It this paper we revisit the fast stylization method introduced in Ulyanov et.
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.
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.
