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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...
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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...
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04/23/2021
Skip-Convolutions for Efficient Video Processing
We propose Skip-Convolutions to leverage the large amount of redundancie...
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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) ...
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03/30/2021
Extending Neural P-frame Codecs for B-frame Coding
While most neural video codecs address P-frame coding (predicting each f...
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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 (...
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02/04/2021
Progressive Neural Image Compression with Nested Quantization and Latent Ordering
We present PLONQ, a progressive neural image compression scheme which pu...
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01/21/2021
Overfitting for Fun and Profit: Instance-Adaptive Data Compression
Neural data compression has been shown to outperform classical methods i...
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05/08/2020
Lossy Compression with Distortion Constrained Optimization
When training end-to-end learned models for lossy compression, one has t...
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04/21/2020
A Data and Compute Efficient Design for Limited-Resources Deep Learning
Thanks to their improved data efficiency, equivariant neural networks ha...
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04/09/2020
Feedback Recurrent Autoencoder for Video Compression
Recent advances in deep generative modeling have enabled efficient model...
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01/30/2020
Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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11/11/2019
Feedback Recurrent AutoEncoder
In this work, we propose a new recurrent autoencoder architecture, terme...
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08/14/2019
Video Compression With Rate-Distortion Autoencoders
In this paper we present a a deep generative model for lossy video compr...
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06/06/2019
Covariance in Physics and Convolutional Neural Networks
In this proceeding we give an overview of the idea of covariance (or equ...
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02/11/2019
Gauge Equivariant Convolutional Networks and the Icosahedral CNN
The idea of equivariance to symmetry transformations provides one of the...
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07/12/2018
Explorations in Homeomorphic Variational Auto-Encoding
The manifold hypothesis states that many kinds of high-dimensional data ...
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07/02/2018
Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
We propose a semantic segmentation model that exploits rotation and refl...
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04/12/2018
3D G-CNNs for Pulmonary Nodule Detection
Convolutional Neural Networks (CNNs) require a large amount of annotated...
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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...
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03/06/2018
HexaConv
The effectiveness of Convolutional Neural Networks stems in large part f...
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01/30/2018
Spherical CNNs
Convolutional Neural Networks (CNNs) have become the method of choice fo...
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02/15/2017
Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
This article presents the prediction difference analysis method for visu...
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12/27/2016
Steerable CNNs
It has long been recognized that the invariance and equivariance propert...
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03/08/2016
A New Method to Visualize Deep Neural Networks
We present a method for visualising the response of a deep neural networ...
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02/24/2016
Group Equivariant Convolutional Networks
We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a...
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05/17/2015
Harmonic Exponential Families on Manifolds
In a range of fields including the geosciences, molecular biology, robot...
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12/24/2014