{"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/stan-spatio-temporal-adversarial-networks-for","title":"STAN: Spatio-Temporal Adversarial Networks for Abnormal Event Detection","arxiv_id":"1804.08381","date":"2018-04-23","proceeding":null,"authors":["Sangmin Lee","Hak Gu Kim","Yong Man Ro"],"abstract":"In this paper, we propose a novel abnormal event detection method with\nspatio-temporal adversarial networks (STAN). We devise a spatio-temporal\ngenerator which synthesizes an inter-frame by considering spatio-temporal\ncharacteristics with bidirectional ConvLSTM. A proposed spatio-temporal\ndiscriminator determines whether an input sequence is real-normal or not with\n3D convolutional layers. These two networks are trained in an adversarial way\nto effectively encode spatio-temporal features of normal patterns. After the\nlearning, the generator and the discriminator can be independently used as\ndetectors, and deviations from the learned normal patterns are detected as\nabnormalities. Experimental results show that the proposed method achieved\ncompetitive performance compared to the state-of-the-art methods. Further, for\nthe interpretation, we visualize the location of abnormal events detected by\nthe proposed networks using a generator loss and discriminator gradients.","url_abs":"http://arxiv.org/abs/1804.08381v1","url_pdf":"http://arxiv.org/pdf/1804.08381v1.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":[],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"event-detection","task_name":"Event Detection"}],"methods":[{"method_slug":"convlstm","method_name":"ConvLSTM"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-shanghaitech","task":"Anomaly Detection","dataset":"ShanghaiTech","model":"STAN","rank_in_archive_order":24,"of":31,"metrics":{"AUC":"76.2%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.08381","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}