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DeepDream

Publications associées (93)

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Measuring Robustness of Classifiers to Geometric Transformations

Can Kanbak

For many classification tasks, the ideal classifier should be invariant to geometric transformations such as changing the view angle. However, this cannot be said decisively for the state-of-the-art image classifiers, such as convolutional neural networks. ...

2017

The Robustness of Deep Networks - A geometric perspective

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Deep neural networks have recently shown impressive classification performance on a diverse set of visual tasks. When deployed in real-world (noise-prone) environments, it is equally important that these classifiers satisfy robustness guarantees: small per ...

Institute of Electrical and Electronics Engineers2017

Robustness of classifiers: from adversarial to random noise

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

Several recent works have shown that state-of-the-art classifiers are vulnerable to worst-case (i.e., adversarial) perturbations of the datapoints. On the other hand, it has been empirically observed that these same classifiers are relatively robust to ran ...

2016

Robust image classification

Alhussein Fawzi

In the past decade, image classification systems have witnessed major advances that led to record performances on challenging datasets. However, little is known about the behavior of these classifiers when the data is subject to perturbations, such as rand ...

EPFL2016

DeepFool: a simple and accurate method to fool deep neural networks

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Alhussein Fawzi

State-of-the-art deep neural networks have achieved impressive results on many image classification tasks. However, these same architectures have been shown to be unstable to small, well sought, perturbations of the images. Despite the importance of this p ...

2016

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Mathieu Salzmann, Fatemehsadat Saleh

Pixel-level annotations are expensive and time consuming to obtain. Hence, weak supervision using only image tags could have a significant impact in semantic segmentation. Recently, CNN-based methods have proposed to fine-tune pre-trained networks using im ...

Springer Int Publishing Ag2016

Reticular Synchorisation (Keynote)

Boris Beaude

En à peine deux décennies, notre capacité à transmettre de l’information fut profondément transformée par le déploiement d’Internet et l’usage de plus en plus généralisé du Web. Plus qu’une technologie de synchronisation, le Web est une puissante technolog ...

2015

Learning Multi-Modal Dictionaries

Pierre Vandergheynst, Rémi Gribonval, Gianluca Monaci, Philippe Jost

Real-world phenomena involve complex interactions between multiple signal modalities. As a consequence, humans are used to integrate at each instant perceptions from all their senses in order to enrich their understanding of the surrounding world. This par ...

2007

Fibre Distributed Sensing for a More Secure Society

Luc Thévenaz

Brillouin time-domain analysis in optical fibres is a novel technique making possible a distributed measurement of temperature and strain over long distance and will deeply modify our view about monitoring large structures, such as dams, bridges, tunnels a ...

Wroclaw University of Technology2006

Optical Fibres for a More Secure Society

Luc Thévenaz

The 21th Century COE in Electrical Engineering and Electronics for the Active and Creative World, The University of Tokyo2005