570 citations · 3k across the 41 of their papers we have counts for
7 papers · 1 filter
Parallelized Rate-Distortion Optimized Quantization Using Deep Learning
Dana Kianfar, Auke Wiggers, Amir Said +2
Rate-Distortion Optimized Quantization (RDOQ) has played an important role in the coding performance of recent video compression standards such as H.264/AVC, H.265/HEVC, VP9 and AV…
Lossy Compression with Distortion Constrained Optimization
Ties van Rozendaal, Guillaume Sautière, Taco S. Cohen
When training end-to-end learned models for lossy compression, one has to balance the rate and distortion losses. This is typically done by manually setting a tradeoff parameter $β…
Feedback Recurrent Autoencoder for Video Compression
Adam Golinski, Reza Pourreza, Yang Yang +2
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems…
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Mirgahney Mohamed, Gabriele Cesa, Taco S. Cohen +1
Thanks to their improved data efficiency, equivariant neural networks have gained increased interest in the deep learning community. They have been successfully applied in the medi…
Adversarial Distortion for Learned Video Compression
Vijay Veerabadran, Reza Pourreza, Amirhossein Habibian +1
In this paper, we present a novel adversarial lossy video compression model. At extremely low bit-rates, standard video coding schemes suffer from unpleasant reconstruction artifac…
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
Pim de Haan, Maurice Weiler, Taco Cohen +1
A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs). Such GCNs utilize isotropic kernels and are theref…