Concept

Mean shift

Related publications (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 LTD2023

Discriminative 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 ...

2022

Hybrid 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 ...

2022

Manifold 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 ...

2022

Learning to Generate the Unknowns as a Remedy to the Open-Set Domain Shift

Mathieu Salzmann

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 SOC2022

Byzantine Fault-Tolerant Distributed Machine Learning with Norm-Based Comparative Gradient Elimination

Nirupam Gupta, Shuo Liu

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 SOC2021

Image Clustering Algorithms to Identify Complicated Cerebral Diseases. Description and Comparison

Maria-Alexandra Paun

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 ...

2020

IMPROVING 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 ...

IEEE2020

Trustworthy Face Recognition: Improving Generalization of Deep Face Presentation Attack Detection

Amir Mohammadi

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 ...

EPFL2020

Learning and leveraging shared domain semantics to counteract visual domain shifts

Róger Bermúdez Chacón

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

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