{"total":20,"rows":[{"id":"2304.12205","title":"Synthetic Datasets for Autonomous Driving: A Survey","created":"2023-04-24","cited_by_count":48,"area":null},{"id":"2205.00664","title":"Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)","created":"2022-05-02","cited_by_count":27,"area":null},{"id":"2212.11368","title":"When and Why Test Generators for Deep Learning Produce Invalid Inputs: an Empirical Study","created":"2022-12-21","cited_by_count":18,"area":null},{"id":"2112.02807","title":"MDPFuzz: Testing Models Solving Markov Decision Processes","created":"2021-12-06","cited_by_count":12,"area":null},{"id":"2112.01955","title":"Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware Criterion","created":"2021-12-03","cited_by_count":11,"area":null},{"id":"2103.05939","title":"A Review and Refinement of Surprise Adequacy","created":"2021-03-10","cited_by_count":9,"area":null},{"id":"2201.05077","title":"Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering","created":"2022-01-13","cited_by_count":8,"area":null},{"id":"2207.10495","title":"Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing","created":"2022-07-21","cited_by_count":8,"area":null},{"id":"2405.11337","title":"A Unified Approach Towards Active Learning and Out-of-Distribution Detection","created":"2024-05-18","cited_by_count":7,"area":null},{"id":"2101.02494","title":"Corner case data description and detection","created":"2021-01-07","cited_by_count":4,"area":null},{"id":"2203.16225","title":"Exploring ML testing in practice -- Lessons learned from an interactive rapid review with Axis Communications","created":"2022-03-30","cited_by_count":3,"area":null},{"id":"2212.07118","title":"Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines","created":"2022-12-14","cited_by_count":3,"area":null},{"id":"2207.03689","title":"Guiding the retraining of convolutional neural networks against adversarial inputs","created":"2022-07-08","cited_by_count":1,"area":null},{"id":"2502.02910","title":"DANDI: Diffusion as Normative Distribution for Deep Neural Network Input","created":"2025-02-05","cited_by_count":1,"area":null},{"id":"2509.17314","title":"Clotho: Measuring Task-Specific Pre-Generation Test Adequacy for LLM Inputs","created":"2025-09-22","cited_by_count":1,"area":null},{"id":"2212.07691","title":"Anomaly Detection in Driving by Cluster Analysis Twice","created":"2022-12-15","cited_by_count":0,"area":null},{"id":"2304.02654","title":"Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks","created":"2023-04-05","cited_by_count":0,"area":null},{"id":"2503.15953","title":"GAN-enhanced Simulation-driven DNN Testing in Absence of Ground Truth","created":"2025-03-20","cited_by_count":0,"area":null},{"id":"2509.03242","title":"TopoMap: A Feature-based Semantic Discriminator of the Topographical Regions in the Test Input Space","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2607.20046","title":"Test Case Prioritization for DNNs via Neural Collapse Instability","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}