{"total":31,"rows":[{"id":"2402.11887","title":"Generative Semi-supervised Graph Anomaly Detection","created":"2024-02-19","cited_by_count":22,"area":null},{"id":"2110.12076","title":"Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review","created":"2021-10-22","cited_by_count":15,"area":null},{"id":"2201.01073","title":"Towards Unsupervised Open World Semantic Segmentation","created":"2022-01-04","cited_by_count":13,"area":null},{"id":"2106.03837","title":"MemStream: Memory-Based Streaming Anomaly Detection","created":"2021-06-07","cited_by_count":12,"area":null},{"id":"2201.05115","title":"Functional Anomaly Detection: a Benchmark Study","created":"2022-01-13","cited_by_count":10,"area":null},{"id":"2311.16514","title":"Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach","created":"2023-11-27","cited_by_count":10,"area":null},{"id":"2203.04453","title":"Machine Learning in NextG Networks via Generative Adversarial Networks","created":"2022-03-09","cited_by_count":8,"area":null},{"id":"2301.13422","title":"Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors","created":"2023-01-31","cited_by_count":8,"area":null},{"id":"2108.11926","title":"Re-using Adversarial Mask Discriminators for Test-time Training under Distribution Shifts","created":"2021-08-26","cited_by_count":7,"area":null},{"id":"2209.11960","title":"Raising the Bar on the Evaluation of Out-of-Distribution Detection","created":"2022-09-24","cited_by_count":7,"area":null},{"id":"2103.15296","title":"Elsa: Energy-based learning for semi-supervised anomaly detection","created":"2021-03-29","cited_by_count":6,"area":null},{"id":"2107.09950","title":"Boundary of Distribution Support Generator (BDSG): Sample Generation on the Boundary","created":"2021-07-21","cited_by_count":6,"area":null},{"id":"2107.11658","title":"Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation","created":"2021-07-24","cited_by_count":6,"area":null},{"id":"2111.12379","title":"Efficient Anomaly Detection Using Self-Supervised Multi-Cue Tasks","created":"2021-11-24","cited_by_count":6,"area":null},{"id":"2303.06302","title":"Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey","created":"2023-03-11","cited_by_count":5,"area":null},{"id":"2108.12280","title":"Stop Throwing Away Discriminators! Re-using Adversaries for Test-Time Training","created":"2021-08-26","cited_by_count":4,"area":null},{"id":"2512.18133","title":"Grad: Guided Relation Diffusion Generation for Graph Augmentation in Graph Fraud Detection","created":"2025-12-19","cited_by_count":4,"area":null},{"id":"2101.04645","title":"Double-Adversarial Activation Anomaly Detection: Adversarial Autoencoders are Anomaly Generators","created":"2021-01-12","cited_by_count":3,"area":null},{"id":"2308.15507","title":"unORANIC: Unsupervised Orthogonalization of Anatomy and Image-Characteristic Features","created":"2023-08-29","cited_by_count":3,"area":null},{"id":"2310.16376","title":"GADY: Unsupervised Anomaly Detection on Dynamic Graphs","created":"2023-10-25","cited_by_count":3,"area":null},{"id":"2106.15504","title":"GraphAnoGAN: Detecting Anomalous Snapshots from Attributed Graphs","created":"2021-06-29","cited_by_count":2,"area":null},{"id":"2110.15273","title":"OMASGAN: Out-of-Distribution Minimum Anomaly Score GAN for Sample Generation on the Boundary","created":"2021-10-28","cited_by_count":2,"area":null},{"id":"2407.07598","title":"Targeted Augmented Data for Audio Deepfake Detection","created":"2024-07-10","cited_by_count":2,"area":null},{"id":"2502.19180","title":"AutoML for Multi-Class Anomaly Compensation of Sensor Drift","created":"2025-02-26","cited_by_count":2,"area":null},{"id":"2408.03441","title":"Simple Perturbations Subvert Ethereum Phishing Transactions Detection: An Empirical Analysis","created":"2024-08-06","cited_by_count":1,"area":null},{"id":"2511.22436","title":"ABounD: Adversarial Boundary-Driven Few-Shot Learning for Multi-Class Anomaly Detection","created":"2025-11-27","cited_by_count":1,"area":null},{"id":"2109.07295","title":"New Perspective on Progressive GANs Distillation for One-class Novelty Detection","created":"2021-09-15","cited_by_count":0,"area":null},{"id":"2112.13514","title":"Anomaly Detection using Capsule Networks for High-dimensional Datasets","created":"2021-12-27","cited_by_count":0,"area":null},{"id":"2207.10324","title":"Enhancing Generative Networks for Chest Anomaly Localization through Automatic Registration-Based Unpaired-to-Pseudo-Paired Training Data Translation","created":"2022-07-21","cited_by_count":0,"area":null},{"id":"2504.17953","title":"Fishing for Phishers: Learning-Based Phishing Detection in Ethereum Transactions","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2508.15256","title":"Normal and Abnormal Pathology Knowledge-Augmented Vision-Language Model for Anomaly Detection in Pathology Images","created":"2025-08-21","cited_by_count":0,"area":null}],"next_cursor":null}