Search Results for author: Florent Forest

Found 8 papers, 4 papers with code

ThermoNeRF: Multimodal Neural Radiance Fields for Thermal Novel View Synthesis

1 code implementation18 Mar 2024 Mariam Hassan, Florent Forest, Olga Fink, Malcolm Mielle

Thermal scene reconstruction exhibit great potential for ap- plications across a broad spectrum of fields, including building energy consumption analysis and non-destructive testing.

Image Generation Novel View Synthesis

Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression

no code implementations24 Jan 2024 Ismail Nejjar, Gaetan Frusque, Florent Forest, Olga Fink

Our approach serves a dual purpose: providing a measure of confidence in predictions and acting as a regularization of the embedding space.

regression Unsupervised Domain Adaptation

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

no code implementations5 Dec 2023 Florent Forest, Olga Fink

However, deep learning models usually only perform well on the data distribution they have been trained on.

Unsupervised Domain Adaptation

Transformer-based conditional generative adversarial network for multivariate time series generation

no code implementations5 Oct 2022 Abdellah Madane, Mohamed-djallel Dilmi, Florent Forest, Hanane Azzag, Mustapha Lebbah, Jerome Lacaille

One of its limitations is that it may generate a random multivariate time series; it may fail to generate samples in the presence of multiple sub-components within an overall distribution.

Data Augmentation Generative Adversarial Network +3

A Survey and Implementation of Performance Metrics for Self-Organized Maps

1 code implementation11 Nov 2020 Florent Forest, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille

Quantitative evaluation of self-organizing maps (SOM) is a subset of clustering validation, which is a challenging problem as such.

Clustering Model Selection +1

Selecting the Number of Clusters $K$ with a Stability Trade-off: an Internal Validation Criterion

1 code implementation15 Jun 2020 Alex Mourer, Florent Forest, Mustapha Lebbah, Hanane Azzag, Jérôme Lacaille

In this perspective, clustering stability has emerged as a natural and model-agnostic principle: an algorithm should find stable structures in the data.

Clustering Model Selection

Deep Embedded SOM: Joint Representation Learning and Self-Organization

1 code implementation ESANN 2019 2019 Florent Forest, Mustapha Lebbah, Hanene Azzag, Jérôme Lacaille

In the wake of recent advances in joint clustering and deep learning, we introduce the Deep Embedded Self-Organizing Map, a model that jointly learns representations and the code vectors of a self-organizing map.

Clustering Dimensionality Reduction +2

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