Concept
Mean shift
Publications associées (32)
Domain knowledge-informed synthetic fault sample generation with health data map for cross-domain planetary gearbox fault diagnosis
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 LTD2023Discriminative clustering with representation learning with any ratio of labeled to unlabeled data
We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...
2022Hybrid Flock - Formation Control Algorithms
Alcherio Martinoli, Cyrill Silvan Baumann, Jonas Perolini, Emna Tourki
Two prominent categories for achieving coordinated multirobot displacement are flocking and navigation in formation. Both categories have their own body of literature and characteristics, including their respective advantages and disadvantages. While typic ...
2022Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models
In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...
2022Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift
In many situations, the data one has access to at test time follows a different distribution from the training data. Over the years, this problem has been tackled by closed-set domain adaptation techniques. Recently, open-set domain adaptation has emerged ...
IEEE COMPUTER SOC2022Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination
This paper considers the Byzantine fault-tolerance problem in distributed stochastic gradient descent (D-SGD) method - a popular algorithm for distributed multi-agent machine learning. In this problem, each agent samples data points independently from a ce ...
IEEE COMPUTER SOC2021Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison
This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two ...
2020IMPROVING CROSS-DATASET PERFORMANCE OF FACE PRESENTATION ATTACK DETECTION SYSTEMS USING FACE RECOGNITION DATASETS
Sébastien Marcel, Amir Mohammadi
Presentation attack detection (PAD) is now considered critically important for any face-recognition (FR) based access-control system. Current deep-learning based PAD systems show excellent performance when they are tested in intra-dataset scenarios. Under ...
IEEE2020Trustworthy Face Recognition: Improving Generalization of Deep Face Presentation Attack Detection
The extremely high recognition accuracy achieved by modern, convolutional neural network (CNN) based face recognition (FR) systems has contributed significantly to the adoption of such systems in a variety of applications, from mundane activities like unlo ...
EPFL2020Learning and leveraging shared domain semantics to counteract visual domain shifts
One of the main limitations of artificial intelligence today is its inability to adapt to unforeseen circumstances. Machine Learning (ML), due to its data-driven nature, is particularly susceptible to this. ML relies on observations in order to learn impli ...
EPFL2020