Shervin Rahimzadeh Arashloo

activity

20242026

collaborators

3 papers

cs.CV2026

Deep Convolutional Large-Margin $ℓ_{p}$ -SVDD for Visual Anomaly Detection

Alireza Dastmalchi Saei, Shervin Rahimzadeh Arashloo

Visual anomaly detection requires adaptive representations and reliable decision boundaries, particularly when anomalous training samples are scarce and class distributions are hig…

cs.LG2025

Manifold-regularised Large-Margin $ℓ_{p}$ -SVDD for Multidimensional Time Series Anomaly Detection

Shervin Rahimzadeh Arashloo

We generalise the recently introduced large-margin $ℓ_{p}$ -SVDD approach to exploit the geometry of data distribution via manifold regularising for time series anomaly detection.…

cs.LG2024

Locally Adaptive One-Class Classifier Fusion with Dynamic $ℓ$ p-Norm Constraints for Robust Anomaly Detection

Sepehr Nourmohammadi, Arda Sarp Yenicesu, Shervin Rahimzadeh Arashloo +1

This paper presents a novel approach to one-class classifier fusion through locally adaptive learning with dynamic $ℓ$ p-norm constraints. We introduce a framework that dynamical…

Deep Convolutional Large-Margin ℓp​-SVDD for Visual Anomaly Detection

Manifold-regularised Large-Margin ℓp​-SVDD for Multidimensional Time Series Anomaly Detection

Locally Adaptive One-Class Classifier Fusion with Dynamic ℓp-Norm Constraints for Robust Anomaly Detection

Deep Convolutional Large-Margin $ℓ_{p}$ -SVDD for Visual Anomaly Detection

Manifold-regularised Large-Margin $ℓ_{p}$ -SVDD for Multidimensional Time Series Anomaly Detection

Locally Adaptive One-Class Classifier Fusion with Dynamic $ℓ$ p-Norm Constraints for Robust Anomaly Detection