Publications related to Generative adversarial network

Full-Gradient Representation for Neural Network Visualization

We introduce a new tool for interpreting neural net responses, namely full-gradients, which decomposes the neural net response into input sensitivity and per-neuron sensitivity components. This is the first proposed representation which satisfies two key p ...

2019

Vulnerability assessment and detection of Deepfake videos

Sébastien Marcel

It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto ...

2019

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks

Sébastien Marcel

Robustness of extracted embeddings in cross-database scenarios is one of the main challenges in text-independent speaker verification (SV) systems. In this paper, we investigate this robustness via performing structural cross-database experiments with or w ...

Idiap2019

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

Daniel Thalmann, Hui Liang

In this paper, we present a novel method for real-time 3D hand pose estimation from single depth images using 3D Convolutional Neural Networks (CNNs). Image-based features extracted by 2D CNNs are not directly suitable for 3D hand pose estimation due to th ...

IEEE COMPUTER SOC2019

A geometry-inspired decision-based attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremen-dous success in image classification. Recent studies havehowever shown that they are easily misled into incorrectclassification decisions by adversarial examples. Adver-saries can even craft attacks by que ...

2019

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Lesya Shchutska, Aurelio Bay, Guido Haefeli, Elena Graverini, Alexey Boyarsky, Alexandre Emmanuel Grandchamp, Sun Hee Kim, Federico Leo Redi, Maria Elena Stramaglia, Oleg Ruchayskiy, Nikolaos Charitonidis, Sebastian Gerlach, Jean-Loup Tastet, Ana Bárbara Rodrigues Cavalcante, Ekaterina Kuznetsova, Hans Dijkstra, Anton Petrov, Sergei Smirnov

This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHIP experiment will be able to search for new long-lived particles produced in a 400 GeV/c SPS proton beam ...

IOP PUBLISHING LTD2019

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Lesya Shchutska, Aurelio Bay, Guido Haefeli, Elena Graverini, Alexey Boyarsky, Sun Hee Kim, Federico Leo Redi, Maria Elena Stramaglia, Oleg Ruchayskiy, Nikolaos Charitonidis, Sebastian Gerlach, Inar Timiryasov, Jean-Loup Tastet, Ana Bárbara Rodrigues Cavalcante, Ekaterina Kuznetsova, Hans Dijkstra, Anton Petrov, Sergei Smirnov

This paper presents a fast approach to simulating muons produced in interactions of the SPS proton beams with the target of the SHiP experiment. The SHiP experiment will be able to search for new long-lived particles produced in a 400~GeV

/c

SPS proton be ...

2019

Adversarial Analytics

Viet Anh Nguyen

Adversarial learning is an emergent technique that provides better security to machine learning systems by deliberately protecting them against specific vulnerabilities of the learning algorithms. Many adversarial learning problems can be cast equivalently ...

EPFL2019

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

Estimation of groundwater storage from seismic data using deep learning

Jan Sickmann Hesthaven

We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks a ...

Wiley2019

Generative adversarial network

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Full-Gradient Representation for Neural Network Visualization

Vulnerability assessment and detection of Deepfake videos

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

A geometry-inspired decision-based attack

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Adversarial Analytics

Biologically plausible deep learning – but how far can we go with shallow networks?

Estimation of groundwater storage from seismic data using deep learning

Biologically plausible deep learning – but how far can we go with shallow networks?

Real-Time 3D Hand Pose Estimation with 3D Convolutional Neural Networks

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks

Adversarial Analytics

A geometry-inspired decision-based attack

Estimation of groundwater storage from seismic data using deep learning

Vulnerability assessment and detection of Deepfake videos

Full-Gradient Representation for Neural Network Visualization

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks

Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks