Chengfang Ren — accepted AI-conference papers

2026

GEPC: Group-Equivariant Posterior Consistency for Out-of-Distribution Detection in Diffusion Models

Rouzoumka Yadang Alexis, Jean Pinsolle, Eugénie TERREAUX, christele morisseau, Jean-Philippe Ovarlez, Chengfang Ren

ICML 2026poster

Diffusion models learn a time-indexed score field $\mathbf{s}_\theta(\mathbf{x}_t,t)$ that often inherits approximate equivariances (flips, rotations, circular shifts) from in-distribution (ID) data and convolutional backbones. Most diffusion-based out-of-distribution (OOD) detectors exploit score m…

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2026

LATENT-SPACE METRICS FOR COMPLEX-VALUED VAE OUT-OF-DISTRIBUTION DETECTION UNDER RADAR CLUTTER

Yadang Alexis Rouzoumka, Eugénie Terreaux, Jean-Philippe Ovarlez, Chengfang Ren

ICASSP 2026poster

We investigate complex-valued Variational AutoEncoders (CVAE) for radar Out-Of-Distribution (OOD) detection in complex radar environments. We proposed several detection metrics: the reconstruction error of CVAE (CVAE-MSE), the latent-based scores (Mahalanobis, Kullback-Leibler divergence (KLD)), and…

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2026

SUPPORT VECTOR DATA DESCRIPTION FOR RADAR TARGET DETECTION

Jean PINSOLLE, Yadang Alexis Rouzoumka, Chengfang Ren, Christèle Morisseau, Jean-Philippe Ovarlez

ICASSP 2026poster

Classical radar detection techniques rely on adaptive detectors that estimate the noise covariance matrix from target-free secondary data. While effective in Gaussian environments, these methods degrade in the presence of clutter, which is better modeled by heavy-tailed distributions such as the Com…

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2025

Out-of-Distribution Radar Detection in Compound Clutter and Thermal Noise through Variational Autoencoders

Yadang Alexis Rouzoumka, Eugénie Terreaux, Christèle Morisseau, Jean Philippe Ovarlez, Chengfang Ren

ICASSP 2025accepted

This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-of-distribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, inclu…

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2024

Through-The-Wall Radar Imaging With Wall Clutter Removal Via Riemannian Optimization On The Fixed-Rank Manifold

Hugo Brehier, Arnaud Breloy, Chengfang Ren, Guillaume Ginolhac

ICASSP 2024accepted

We introduce a new method for Through-the-Wall Radar Imaging (TWRI) that detects the location of stationary targets hidden by a wall. A crucial step is the mitigation of wall returns which obscure the scene and which are characterized by their low-rankedness given the radar measurement setup. Wherea…

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2023

Large Dimensional Analysis of LS-SVM Transfer Learning: Application to Polsar Classification

Cyprien Doz, Chengfang Ren, Jean Philippe Ovarlez, Romain Couillet

ICASSP 2023accepted

This article analyzes a kernel-based transfer learning method, under a k-class Gaussian mixture model for the input data. Following recent advances in random matrix theory, we propose new insights in transfer learning schemes for challenging cases, when the first-order statistics of all data classes…

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2022

On the Use of Geodesic Triangles between Gaussian Distributions for Classification Problems

Antoine Collas, Florent Bouchard, Guillaume Ginolhac, Arnaud Breloy, Chengfang Ren, Jean Philippe Ovarlez

ICASSP 2022accepted

This paper presents a new classification framework for both first and second order statistics, i.e. mean/location and covariance matrix. In the last decade, several covariance matrix classification algorithms have been proposed. They often leverage the Riemannian geometry of symmetric positive defin…

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2018

Efficient Estimation of Scatter Matrix with Convex Structure Under $T$ -Distribution

Bruno Meriaux, Chengfang Ren, Mohammed Nabil El Korso, Arnaud Breloy, Philippe Forster

ICASSP 2018accepted

This paper addresses structured covariance matrix estimation under t -distribution. Covariance matrices frequently reveal a particular structure due to the considered application and taking into account this structure usually improves estimation accuracy. In the framework of robust estimation, the t…

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2015

A constrained hybrid Cramér-Rao bound for parameter estimation

Chengfang Ren, Julien Le Kernec, Jérôme Galy, Eric Chaumette, Pascal Larzabal, Alexandre Renaux

ICASSP 2015accepted

In statistical signal processing, hybrid parameter estimation refers to the case where the parameters vector to estimate contains both non-random and random parameters. Numerous works have shown the versatility of deterministic constrained Cramér-Rao bound for estimation performance analysis and des…

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