Publications related to DeepDream

Deep Image Restoration: Between Data Fidelity and Learned Priors

Image restoration reconstructs, as faithfully as possible, an original image from a potentially degraded version of it. Image degradations can be of various types, for instance haze, unwanted reflections, optical or spectral aberrations, or other physicall ...

EPFL2021

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Kaicheng Yu

Artificial intelligence has been an ultimate design goal since the inception of computers decades ago. Among the many attempts towards general artificial intelligence, modern machine learning successfully tackles many complex problems thanks to the progres ...

EPFL2021

Biologically plausible unsupervised learning in shallow and deep neural networks

Bernd Albert Illing

The way our brain learns to disentangle complex signals into unambiguous concepts is fascinating but remains largely unknown. There is evidence, however, that hierarchical neural representations play a key role in the cortex. This thesis investigates biolo ...

EPFL2021

Investigation of Lead-free perovskites for Optoelectronic Application

Alexander Fedorovskiy

The search for new energy sources and materials is at the foundations of scientific and technological progress. These two, often interconnected, fundamental elements open a way to create novel designs and improve existing ones for all types of machines and ...

EPFL2021

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

Shape Reconstruction by Learning Differentiable Surface Representations

Pascal Fua, Mathieu Salzmann, Shaifali Parashar, Jan Bednarík, Erhan Gündogdu

Generative models that produce point clouds have emerged as a powerful tool to represent 3D surfaces, and the best current ones rely on learning an ensemble of parametric representations. Unfortunately, they offer no control over the deformations of the su ...

2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Pascal Fua, Mathieu Salzmann, Agata Justyna Mosinska, Mateusz Kozinski

The difficulty of obtaining annotations to build training databases still slows down the adoption of recent deep learning approaches for biomedical image analysis. In this paper, we show that we can train a Deep Net to perform 3D volumetric delineation giv ...

ELSEVIER2020

Single-Stage 6D Object Pose Estimation

Pascal Fua, Mathieu Salzmann, Wei Wang, Yinlin Hu

Most recent 6D pose estimation frameworks first rely on a deep network to establish correspondences between 3D object keypoints and 2D image locations and then use a variant of a RANSAC-based Perspective-n-Point (PnP) algorithm. This two-stage process, how ...

2020

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Wei Wang

While several research studies have focused on analyzing human behavior and, in particular, emotional signals from visual data, the problem of synthesizing face video sequences with specific attributes (e.g. age, facial expressions) received much less atte ...

2020

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Deep Image Restoration: Between Data Fidelity and Learned Priors

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Biologically plausible unsupervised learning in shallow and deep neural networks

Investigation of Lead-free perovskites for Optoelectronic Application

Neural Anisotropy Directions

Shape Reconstruction by Learning Differentiable Surface Representations

Square Attack: a query-efficient black-box adversarial attack via random search

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Single-Stage 6D Object Pose Estimation

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Deep Image Restoration: Between Data Fidelity and Learned Priors

From Human-Designed Convolutional Neural Networks Towards Robust Neural Architecture Search

Tracing in 2D to reduce the annotation effort for 3D deep delineation of linear structures

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Investigation of Lead-free perovskites for Optoelectronic Application

Biologically plausible unsupervised learning in shallow and deep neural networks

Shape Reconstruction by Learning Differentiable Surface Representations

Single-Stage 6D Object Pose Estimation

Neural Anisotropy Directions

Square Attack: a query-efficient black-box adversarial attack via random search