Publications associées à Domaine public (propriété intellectuelle)

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

2024

Test-time adaptation for 6D pose tracking

Andrea Cavallaro

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

Elsevier Sci Ltd2024

Data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins

Paolo De Los Rios, Alessandro Barducci, Stefano Zamuner, Mathieu Rebeaud, Duccio Malinverni

The 70-kD heat shock protein (Hsp70) chaperone system is a central hub of the proteostasis network that helps maintain protein homeostasis in all organisms. The recruitment of Hsp70 to perform different and specific cellular functions is regulated by the J ...

National Academy of Sciences2023

Center-aware Adversarial Augmentation for Single Domain Generalization

Mathieu Salzmann, Zhiye Wang

Domain generalization (DG) aims to learn a model from multiple training (i.e., source) domains that can generalize well to the unseen test (i.e., target) data coming from a different distribution. Single domain generalization (SingleDG) has recently emerge ...

IEEE COMPUTER SOC2023

Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis

Olga Fink, Jongmoon Ha

Extensive research has been conducted on fault diagnosis of planetary gearboxes using vibration signals and deep learning (DL) approaches. However, DL-based methods are susceptible to the domain shift problem caused by varying operating conditions of the g ...

ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD2023

TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar

Most recent test-time adaptation methods focus on only classification tasks, use specialized network architectures, destroy model calibration or rely on lightweight information from the source domain. To tackle these issues, this paper proposes a novel Tes ...

Los Alamitos2023

On Artificial Intelligence and Manipulation

Marcello Ienca

The increasing diffusion of novel digital and online sociotechnical systems for arational behavioral influence based on Artificial Intelligence (AI), such as social media, microtargeting advertising, and personalized search algorithms, has brought about ne ...

SPRINGER2023

J-domain protein chaperone circuits in proteostasis and disease

Paolo De Los Rios, Duccio Malinverni, Ruobing Zhang

The J-domain proteins (JDP) form the largest protein family among cellular chaperones. In cooperation with the Hsp70 chaperone system, these co-chaperones orchestrate a plethora of distinct functions, including those that help maintain cellular proteostasi ...

ELSEVIER SCIENCE LONDON2022

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Jean-Philippe Thiran, Guillaume Marc Georges Vray, Devavrat Tomar, Seyedbehzad Bozorgtabar, Manana Lortkipanidze

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Institute of Electrical and Electronics Engineers2021

Deep Domain Adaptation in Earth Observation

Devis Tuia, Benjamin Alexander Kellenberger, Nicolas Courty

When applied to new datasets, acquired at different time moments, with different sensors or under different acquisition conditions, deep learning models might fail spectacularly. This is because they have learned from the data distribution observed during ...

Wiley2021

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

Claudiu-Cristian Musat, Diego Matteo Antognini, Martin Milenkoski

In this paper, we describe a method to tackle data sparsity and create recommendations in domains with limited knowledge about user preferences. We expand the variational autoencoder collaborative filtering from a single-domain to a multi-domain setting. T ...

2021

Design and fabrication of an aerosol collector for infrared spectroscopy analysis

Nikunj Dudani

Particulate matter (PM) pollution causes adverse health effects and millions of deaths each year. PM or aerosol, is difficult to characterize because of its wide range of particle sizes, constituents (various organic and inorganic compounds), concentration ...

EPFL2021

ExprADA: Adversarial domain adaptation for facial expression analysis

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

We propose a deep neural network based image-to-image translation for domain adaptation, which aims at finding translations between image domains. Despite recent GAN based methods showing promising results in image-to-image translation, they are prone to f ...

2020

Iris Liveness Detection Competition (LivDet-Iris) – The 2020 Edition

Sébastien Marcel, Amir Mohammadi, Priyanka Das

Launched in 2013, LivDet-Iris is an international competition series open to academia and industry with the aim to assess and report advances in iris Presentation Attack Detection (PAD). This paper presents results from the fourth competition of the series ...

IEEE2020

Deformation-aware Unpaired Image Translation for Pose Estimation on Laboratory Animals

Pascal Fua, Pavan P Ramdya, Helge Jochen Rhodin, Semih Günel, Siyuan Li, Mirela Ostrek

Our goal is to capture the pose of neuroscience model organisms, without using any manual supervision, to be able to study how neural circuits orchestrate behaviour. Human pose estimation attains remarkable accuracy when trained on real or simulated datase ...

2020

The Role of Reynolds Number Effect and Tip Leakage in Compressor Geometry Scaling at Low Turbulent Reynolds Numbers

Jürg Alexander Schiffmann, Markus Diehl, Christoph Schreiber

In compressor design, a convenient way to save time is to scale an existing geometry to required specifications, rather than developing a new design. The approach works well when scaling compressors of similar size at high Reynolds numbers but becomes more ...

2020

Learning How To Recognize Faces In Heterogeneous Environments

Tiago De Freitas Pereira

Face recognition is a mature field in biometrics in which several systems have been proposed over the last three decades. Such systems are extremely reliable under controlled recording conditions and it has been deployed in the field in critical tasks, suc ...

EPFL2019

110th Anniversary: From Solubility Parameters to Predictive Equation-of-State Modeling

Vassily Hatzimanikatis

A simple predictive molecular thermodynamic model for bulk phases and interfaces is presented. The model combines features of the quantitative structure-property relationships (QSPR) approach, the equation-of-state approach, and quantum-chemical calculatio ...

AMER CHEMICAL SOC2019

Beyond Sharing Weights for Deep Domain Adaptation

Pascal Fua, Mathieu Salzmann, Artem Rozantsev

The performance of a classifier trained on data coming from a specific domain typically degrades when applied to a related but different one. While annotating many samples from the new domain would address this issue, it is often too expensive or impractic ...

2018

Narrative Recomposition in the Context of Digital Reading

Cyril Antoine Michel Bornet

In any creative process, the tools one uses have an immediate influence on the shape of the final artwork. However, while the digital revolution has redefined core values in most creative domains over the last few decades, its impact on literature remains ...

EPFL2017