3.1k citations · 8.3k across the 43 of their papers we have counts for
6 papers · 1 filter
Deep Image Prior
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic i…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…
Deep multi-frame face super-resolution
E. Ustinova, V. Lempitsky
Face verification and recognition problems have seen rapid progress in recent years, however recognition from small size images remains a challenging task that is inherently intert…
It Takes (Only) Two: Adversarial Generator-Encoder Networks
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and uncondition…
Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models
Roman Klokov, Victor Lempitsky
We present a new deep learning architecture (called Kd-network) that is designed for 3D model recognition tasks and works with unstructured point clouds. The new architecture perfo…
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
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 genera…