{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/dasvdd-deep-autoencoding-support-vector-data","title":"DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly Detection","arxiv_id":"2106.05410","date":"2021-06-09","proceeding":null,"authors":["Hadi Hojjati","Narges Armanfard"],"abstract":"Semi-supervised anomaly detection aims to detect anomalies from normal samples using a model that is trained on normal data. With recent advancements in deep learning, researchers have designed efficient deep anomaly detection methods. Existing works commonly use neural networks to map the data into a more informative representation and then apply an anomaly detection algorithm. In this paper, we propose a method, DASVDD, that jointly learns the parameters of an autoencoder while minimizing the volume of an enclosing hyper-sphere on its latent representation. We propose an anomaly score which is a combination of autoencoder's reconstruction error and the distance from the center of the enclosing hypersphere in the latent representation. Minimizing this anomaly score aids us in learning the underlying distribution of the normal class during training. Including the reconstruction error in the anomaly score ensures that DASVDD does not suffer from the common hypersphere collapse issue since the DASVDD model does not converge to the trivial solution of mapping all inputs to a constant point in the latent representation. Experimental evaluations on several benchmark datasets show that the proposed method outperforms the commonly used state-of-the-art anomaly detection algorithms while maintaining robust performance across different anomaly classes.","url_abs":"https://arxiv.org/abs/2106.05410v4","url_pdf":"https://arxiv.org/pdf/2106.05410v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"dasvdd-deep-autoencoding-support-vector-data","repo_url":"https://github.com/Armanfard-Lab/DASVDD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fashion-mnist","task":"Anomaly Detection","dataset":"Fashion-MNIST","model":"DASVDD","rank_in_archive_order":7,"of":12,"metrics":{"ROC AUC":"92.6"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mnist","task":"Anomaly Detection","dataset":"MNIST","model":"DASVDD","rank_in_archive_order":4,"of":6,"metrics":{"ROC AUC":"97.7"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-10","task":"Anomaly Detection","dataset":"One-class CIFAR-10","model":"DASVDD","rank_in_archive_order":33,"of":36,"metrics":{"AUROC":"66.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2106.05410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05410"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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