Bilkent University
Department of Computer Engineering
CS 590/690 SEMINAR
Deep Large-Margin lp-SVDD with CNN Feature Learning for Novelty Detection
Alireza Dastmalchi Saei
Master Student
(Supervisor:Asst.Prof. Shervin R. Arashloo)
Computer Engineering Department
Bilkent University
Abstract: Compared with other approaches, the recently proposed lp-norm large-margin Support Vector Data Description (SVDD) method has demonstrated superior performance for novelty detection across diverse evaluation settings. Nevertheless, because it is designed to operate on fixed features, it inevitably decouples feature extraction from classifier training, leading to suboptimal performance. In this study, we extend the large-margin lp-SVDD approach by optimizing the objective function while jointly learning deep convolutional features, thereby minimizing classification errors and improving overall performance. To this end, we propose a stable alternating optimization scheme that performs classifier boundary fitting via a Frank-Wolfe-based algorithm, while a robust primal-based strategy guides CNN updates. Extensive experiments on several widely used novelty detection benchmarks demonstrate the superiority of the proposed approach over both the baseline and recent methods, even when only limited amounts of anomaly training data are available.
DATE: March 09, Monday @ 15:50 Place: EA 502