Taco S Cohen | DeepAI

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∙ 03/03/2022

Region-of-Interest Based Neural Video Compression

Humans do not perceive all parts of a scene with the same resolution, bu...

0 Yura Perugachi-Diaz, et al. ∙

∙ 11/19/2021

Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set

We introduce a video compression algorithm based on instance-adaptive le...

2 Ties van Rozendaal, et al. ∙

∙ 04/23/2021

Skip-Convolutions for Efficient Video Processing

We propose Skip-Convolutions to leverage the large amount of redundancie...

8 Amirhossein Habibian, et al. ∙

∙ 04/01/2021

A Combined Deep Learning based End-to-End Video Coding Architecture for YUV Color Space

Most of the existing deep learning based end-to-end video coding (DLEC) ...

0 Ankitesh K. Singh, et al. ∙

∙ 03/30/2021

Extending Neural P-frame Codecs for B-frame Coding

While most neural video codecs address P-frame coding (predicting each f...

0 Reza Pourreza, et al. ∙

∙ 02/27/2021

Transform Network Architectures for Deep Learning based End-to-End Image/Video Coding in Subsampled Color Spaces

Most of the existing deep learning based end-to-end image/video coding (...

0 Hilmi E. Egilmez, et al. ∙

∙ 02/04/2021

Progressive Neural Image Compression with Nested Quantization and Latent Ordering

We present PLONQ, a progressive neural image compression scheme which pu...

0 Yadong Lu, et al. ∙

∙ 01/21/2021

Overfitting for Fun and Profit: Instance-Adaptive Data Compression

Neural data compression has been shown to outperform classical methods i...

5 Ties van Rozendaal, et al. ∙

∙ 05/08/2020

Lossy Compression with Distortion Constrained Optimization

When training end-to-end learned models for lossy compression, one has t...

0 Ties van Rozendaal, et al. ∙

∙ 04/21/2020

A Data and Compute Efficient Design for Limited-Resources Deep Learning

Thanks to their improved data efficiency, equivariant neural networks ha...

4 Mirgahney Mohamed, et al. ∙

∙ 04/09/2020

Feedback Recurrent Autoencoder for Video Compression

Recent advances in deep generative modeling have enabled efficient model...

5 Adam Golinski, et al. ∙

∙ 01/30/2020

Learning Discrete Distributions by Dequantization

Media is generally stored digitally and is therefore discrete. Many succ...

16 Emiel Hoogeboom, et al. ∙

∙ 11/11/2019

Feedback Recurrent AutoEncoder

In this work, we propose a new recurrent autoencoder architecture, terme...

0 Yang Yang, et al. ∙

∙ 08/14/2019

Video Compression With Rate-Distortion Autoencoders

In this paper we present a a deep generative model for lossy video compr...

7 Amirhossein Habibian, et al. ∙

∙ 06/06/2019

Covariance in Physics and Convolutional Neural Networks

In this proceeding we give an overview of the idea of covariance (or equ...

1 Miranda C. N. Cheng, et al. ∙

∙ 02/11/2019

Gauge Equivariant Convolutional Networks and the Icosahedral CNN

The idea of equivariance to symmetry transformations provides one of the...

12 Taco S Cohen, et al. ∙

∙ 07/12/2018

Explorations in Homeomorphic Variational Auto-Encoding

The manifold hypothesis states that many kinds of high-dimensional data ...

2 Luca Falorsi, et al. ∙

∙ 07/02/2018

Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks

We propose a semantic segmentation model that exploits rotation and refl...

0 Jasper Linmans, et al. ∙

∙ 04/12/2018

3D G-CNNs for Pulmonary Nodule Detection

Convolutional Neural Networks (CNNs) require a large amount of annotated...

0 Marysia Winkels, et al. ∙

∙ 03/28/2018

Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)

Group equivariant and steerable convolutional neural networks (regular a...

0 Taco S Cohen, et al. ∙

∙ 03/06/2018

HexaConv

The effectiveness of Convolutional Neural Networks stems in large part f...

0 Emiel Hoogeboom, et al. ∙

∙ 01/30/2018

Spherical CNNs

Convolutional Neural Networks (CNNs) have become the method of choice fo...

0 Taco S Cohen, et al. ∙

∙ 02/15/2017

Visualizing Deep Neural Network Decisions: Prediction Difference Analysis

This article presents the prediction difference analysis method for visu...

0 Luisa M Zintgraf, et al. ∙

∙ 12/27/2016

Steerable CNNs

It has long been recognized that the invariance and equivariance propert...

0 Taco S Cohen, et al. ∙

∙ 03/08/2016

A New Method to Visualize Deep Neural Networks

We present a method for visualising the response of a deep neural networ...

0 Luisa M Zintgraf, et al. ∙

∙ 02/24/2016

Group Equivariant Convolutional Networks

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a...

0 Taco S Cohen, et al. ∙

∙ 05/17/2015

Harmonic Exponential Families on Manifolds

In a range of fields including the geosciences, molecular biology, robot...

0 Taco S Cohen, et al. ∙

∙ 12/24/2014

Transformation Properties of Learned Visual Representations

When a three-dimensional object moves relative to an observer, a change ...

0 Taco S Cohen, et al. ∙

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