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Neural Distributed Image Compression with Cross-Attention Feature Alignment

Related publications (48)

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Towards neural network approaches for point cloud compression

Touradj Ebrahimi, Evangelos Alexiou, Kuan Tung

Point cloud imaging has emerged as an efficient and popular solution to represent immersive visual information. However, the large volume of data generated in the acquisition process reveals the need of efficient compression solutions in order to store and ...

2020

Disentangling feature and lazy training in deep neural networks

Matthieu Wyart, Mario Geiger, Stefano Spigler, Arthur Jacot

Two distinct limits for deep learning have been derived as the network width h -> infinity, depending on how the weights of the last layer scale with h. In the neural tangent Kernel (NTK) limit, the dynamics becomes linear in the weights and is described b ...

IOP PUBLISHING LTD2020

Noise-Resilient and Interpretable Epileptic Seizure Detection

David Atienza Alonso, Amir Aminifar, Anthony Hitchcock Thomas

Deep convolutional neural networks have recently emerged as a state-of-the art tool in detection of seizures. Such models offer the ability to extract complex nonlinear representations of an electroencephalogram (EEG) signal which can improve accuracy over ...

IEEE2020

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

Harshit Gupta

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

EPFL2020

Second-order information, in the form of Hessian- or Inverse-Hessian-vector products, is a fundamental tool for solving optimization problems. Recently, there has been a tremendous amount of work on utilizing this information for the current compute and me ...

2020

Contextual semantic interpretability

Devis Tuia, Sylvain Lobry, Nicolas Courty

Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in t ...

LNCS2020

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

An improved objective metric to predict image quality using deep neural networks

Touradj Ebrahimi, Pinar Akyazi

Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neur ...

2019

Entropy and mutual information in models of deep neural networks

Nicolas Macris, Florent Gérard Krzakala, Lenka Zdeborová, Jean François Emmanuel Barbier, Clément Dominique Luneau

We examine a class of stochastic deep learning models with a tractable method to compute information-theoretic quantities. Our contributions are three-fold: (i) we show how entropies and mutual informations can be derived from heuristic statistical physics ...

IOP PUBLISHING LTD2019

This paper presents a novel deep architecture for weakly-supervised temporal action localization that not only generates segment-level action responses but also propagates segment-level responses to the neighborhood in a form of graph Laplacian regularizat ...

IEEE2019