
SkipConvolutions for Efficient Video Processing
We propose SkipConvolutions to leverage the large amount of redundancie...
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A Combined Deep Learning based EndtoEnd Video Coding Architecture for YUV Color Space
Most of the existing deep learning based endtoend video coding (DLEC) ...
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Extending Neural Pframe Codecs for Bframe Coding
While most neural video codecs address Pframe coding (predicting each f...
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Transform Network Architectures for Deep Learning based EndtoEnd Image/Video Coding in Subsampled Color Spaces
Most of the existing deep learning based endtoend image/video coding (...
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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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Overfitting for Fun and Profit: InstanceAdaptive Data Compression
Neural data compression has been shown to outperform classical methods i...
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Lossy Compression with Distortion Constrained Optimization
When training endtoend learned models for lossy compression, one has t...
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A Data and Compute Efficient Design for LimitedResources Deep Learning
Thanks to their improved data efficiency, equivariant neural networks ha...
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Feedback Recurrent Autoencoder for Video Compression
Recent advances in deep generative modeling have enabled efficient model...
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Learning Discrete Distributions by Dequantization
Media is generally stored digitally and is therefore discrete. Many succ...
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Feedback Recurrent AutoEncoder
In this work, we propose a new recurrent autoencoder architecture, terme...
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Video Compression With RateDistortion Autoencoders
In this paper we present a a deep generative model for lossy video compr...
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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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Gauge Equivariant Convolutional Networks and the Icosahedral CNN
The idea of equivariance to symmetry transformations provides one of the...
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Explorations in Homeomorphic Variational AutoEncoding
The manifold hypothesis states that many kinds of highdimensional data ...
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Sample Efficient Semantic Segmentation using Rotation Equivariant Convolutional Networks
We propose a semantic segmentation model that exploits rotation and refl...
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3D GCNNs for Pulmonary Nodule Detection
Convolutional Neural Networks (CNNs) require a large amount of annotated...
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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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HexaConv
The effectiveness of Convolutional Neural Networks stems in large part f...
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Spherical CNNs
Convolutional Neural Networks (CNNs) have become the method of choice fo...
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Visualizing Deep Neural Network Decisions: Prediction Difference Analysis
This article presents the prediction difference analysis method for visu...
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Steerable CNNs
It has long been recognized that the invariance and equivariance propert...
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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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Group Equivariant Convolutional Networks
We introduce Group equivariant Convolutional Neural Networks (GCNNs), a...
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Harmonic Exponential Families on Manifolds
In a range of fields including the geosciences, molecular biology, robot...
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Transformation Properties of Learned Visual Representations
When a threedimensional object moves relative to an observer, a change ...
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Taco S Cohen
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