{"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/gan-based-anomaly-detection-in-imbalance","title":"GAN-based Anomaly Detection in Imbalance Problems","arxiv_id":null,"date":"2020-08-28","proceeding":null,"authors":["Junbong Kim","Kwanghee Jeong","Hyomin Choi","and Kisung Seo"],"abstract":"Imbalance problems in object detection are one of the key\r\nissues that affect the performance greatly. Our focus in this work is to\r\naddress an imbalance problem arising from defect detection in industrial inspections, including the different number of defect and non-defect\r\ndataset, the gap of distribution among defect classes, and various sizes\r\nof defects. To this end, we adopt the anomaly detection method that is\r\nto identify unusual patterns to address such challenging problems. Especially generative adversarial network (GAN) and autoencoder-based\r\napproaches have shown to be effective in this field. In this work, 1) we\r\npropose a novel GAN-based anomaly detection model which consists of\r\nan autoencoder as the generator and two separate discriminators for\r\neach of normal and anomaly input; and 2) we also explore a way to effectively optimize our model by proposing new loss functions: Patch loss and\r\nAnomaly adversarial loss, and further combining them to jointly train\r\nthe model. In our experiment, we evaluate our model on conventional\r\nbenchmark datasets such as MNIST, Fashion MNIST, CIFAR 10/100\r\ndata as well as on real-world industrial dataset – smartphone case defects. Finally, experimental results demonstrate the effectiveness of our\r\napproach by showing the results of outperforming the current State-OfThe-Art approaches in terms of the average area under the ROC curve\r\n(AUROC).","url_abs":"http://intlab.skuniv.ac.kr/paper/GAN-based_Anomaly_Detection_in_Imbalance_Problems.pdf","url_pdf":"http://intlab.skuniv.ac.kr/paper/GAN-based_Anomaly_Detection_in_Imbalance_Problems.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":"defect-detection","task_name":"Defect Detection"},{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fashion-mnist","task":"Anomaly Detection","dataset":"Fashion-MNIST","model":"GAN based Anomaly Detection in Imbalance Problems","rank_in_archive_order":1,"of":12,"metrics":{"ROC AUC":"98.6"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-mnist","task":"Anomaly Detection","dataset":"MNIST","model":"GAN-based Anomaly Detection in Imbalance\nProblems","rank_in_archive_order":1,"of":6,"metrics":{"ROC AUC":"99.7"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-10","task":"Anomaly Detection","dataset":"One-class CIFAR-10","model":"GAN based Anomaly Detection in Imbalance Problems","rank_in_archive_order":18,"of":36,"metrics":{"AUROC":"90.6"},"uses_additional_data":true},{"leaderboard":"/sota/anomaly-detection-on-one-class-cifar-100","task":"Anomaly Detection","dataset":"One-class CIFAR-100","model":"GAN based Anomaly Detection in Imbalance Problems","rank_in_archive_order":7,"of":15,"metrics":{"AUROC":"87.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}