Publications associées à Réseaux antagonistes génératifs

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Pascal Fua, Mateusz Kozinski, Leonardo Citraro, Subeesh Vasu

Most state-of-the-art approaches to road extraction from aerial images rely on a CNN trained to label road pixels as foreground and remainder of the image as background. The CNN is usually trained by minimizing pixel-wise losses, which is less than ideal t ...

2020

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Alexis Berne, Jussi Sakari Leinonen, Daniele Nerini

Generative adversarial networks (GANs) have been recently adopted for super-resolution, an application closely related to what is referred to as "downscaling'' in the atmospheric sciences: improving the spatial resolution of low-resolution images. The abil ...

2020

Lyrics-Conditioned Neural Melody Generation

Simon Canales, Yi Yu

Generating melody from lyrics to compose a song has been a very interesting research topic in the area of artificial intelligence and music, which tries to predict generative music relationship between lyrics and melody. In this demonstration paper, by exp ...

SPRINGER INTERNATIONAL PUBLISHING AG2020

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

Sébastien Marcel, Eklavya Sarkar, Laurent Colbois

Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. ...

Idiap2020

Mixed-Precision Deep Learning Based on Computational Memory

Irem Boybat Kara, Evangelos Eleftheriou, Abu Sebastian

Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has ...

FRONTIERS MEDIA SA2020

MPGM: Scalable and Accurate Multiple Network Alignment

Matthias Grossglauser, Ehsan Kazemi

Protein-protein interaction (PPI) network alignment is a canonical operation to transfer biological knowledge among species. The alignment of PPI-networks has many applications, such as the prediction of protein function, detection of conserved network mot ...

IEEE COMPUTER SOC2020

Neural Anisotropy Directions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

In this work, we analyze the role of the network architecture in shaping the inductive bias of deep classifiers. To that end, we start by focusing on a very simple problem, i.e., classifying a class of linearly separable distributions, and show that, depen ...

2020

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

Jie Luo, Yiyu Wang, Mengshi Qi

Predictive scene parsing is a task of assigning pixel-level semantic labels to a future frame of a video. It has many applications in vision-based artificial intelligent systems, e.g., autonomous driving and robot navigation. Although previous work has sho ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

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Réseaux antagonistes génératifs

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Lyrics-Conditioned Neural Melody Generation

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

Mixed-Precision Deep Learning Based on Computational Memory

MPGM: Scalable and Accurate Multiple Network Alignment

Neural Anisotropy Directions

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

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

Graph Chatbot

Chattez avec Graph Search

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

STC-GAN: Spatio-Temporally Coupled Generative Adversarial Networks for Predictive Scene Parsing

TopoAL: An Adversarial Learning Approach for Topology-Aware Road Segmentation

Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields With a Generative Adversarial Network

Mixed-Precision Deep Learning Based on Computational Memory

Lyrics-Conditioned Neural Melody Generation

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

MPGM: Scalable and Accurate Multiple Network Alignment

Neural Anisotropy Directions

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