{"list":"other","total":68,"rows":[{"id":"1405.0312","title":"Microsoft COCO: Common Objects in Context","created":"2014-05-01","cited_by_count":17608},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1711.03938","title":"CARLA: An Open Urban Driving Simulator","created":"2017-11-10","cited_by_count":2449},{"id":"1609.04802","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","created":"2016-09-15","cited_by_count":1908},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1706.02690","title":"Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks","created":"2017-06-08","cited_by_count":1328},{"id":"2111.05826","title":"Palette: Image-to-Image Diffusion Models","created":"2021-11-10","cited_by_count":1300},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"2111.09883","title":"Swin Transformer V2: Scaling Up Capacity and Resolution","created":"2021-11-18","cited_by_count":1228},{"id":"1609.05158","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","created":"2016-09-16","cited_by_count":1204},{"id":"2107.10833","title":"Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data","created":"2021-07-22","cited_by_count":970},{"id":"2110.11334","title":"Generalized Out-of-Distribution Detection: A Survey","created":"2021-10-21","cited_by_count":802},{"id":"1904.07734","title":"Three scenarios for continual learning","created":"2019-04-15","cited_by_count":614},{"id":"1802.10501","title":"Predictive Uncertainty Estimation via Prior Networks","created":"2018-02-28","cited_by_count":557},{"id":"2108.13624","title":"Towards Out-Of-Distribution Generalization: A Survey","created":"2021-08-31","cited_by_count":436},{"id":"2002.11297","title":"Generalized ODIN: Detecting Out-of-distribution Image without Learning from Out-of-distribution Data","created":"2020-02-26","cited_by_count":408},{"id":"2203.10807","title":"ViM: Out-Of-Distribution with Virtual-logit Matching","created":"2022-03-21","cited_by_count":405},{"id":"1902.06068","title":"Deep Learning for Image Super-resolution: A Survey","created":"2019-02-16","cited_by_count":369},{"id":"1901.09192","title":"SelectiveNet: A Deep Neural Network with an Integrated Reject Option","created":"2019-01-26","cited_by_count":350},{"id":"1711.11157","title":"A Semantic Loss Function for Deep Learning with Symbolic Knowledge","created":"2017-11-29","cited_by_count":270},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"1810.11953","title":"Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift","created":"2018-10-29","cited_by_count":243},{"id":"1705.07832","title":"Concrete Dropout","created":"2017-05-22","cited_by_count":227},{"id":"1802.09088","title":"Adversarially Learned One-Class Classifier for Novelty Detection","created":"2018-02-25","cited_by_count":202},{"id":"2010.05063","title":"Adaptive Aggregation Networks for Class-Incremental Learning","created":"2020-10-10","cited_by_count":149},{"id":"1912.13171","title":"Deep Learning on Image Denoising: An overview","created":"2019-12-31","cited_by_count":128},{"id":"1907.06890","title":"A General Framework for Uncertainty Estimation in Deep Learning","created":"2019-07-16","cited_by_count":119},{"id":"2107.11277","title":"Machine Learning with a Reject Option: A survey","created":"2021-07-23","cited_by_count":109},{"id":"1705.08968","title":"Logic Tensor Networks for Semantic Image Interpretation","created":"2017-05-24","cited_by_count":92},{"id":"1812.02765","title":"Improving Reconstruction Autoencoder Out-of-distribution Detection with Mahalanobis Distance","created":"2018-12-06","cited_by_count":74},{"id":"2110.12899","title":"No One Representation to Rule Them All: Overlapping Features of Training Methods","created":"2021-10-20","cited_by_count":58},{"id":"1910.04443","title":"Misbehaviour Prediction for Autonomous Driving Systems","created":"2019-10-10","cited_by_count":55},{"id":"2106.02395","title":"DOCTOR: A Simple Method for Detecting Misclassification Errors","created":"2021-06-04","cited_by_count":53},{"id":"1610.01256","title":"On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products","created":"2016-10-05","cited_by_count":52},{"id":"1805.10915","title":"Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification","created":"2018-05-28","cited_by_count":51},{"id":"1812.05793","title":"Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing","created":"2018-12-14","cited_by_count":48},{"id":"1807.11037","title":"Efficient Uncertainty Estimation for Semantic Segmentation in Videos","created":"2018-07-29","cited_by_count":46},{"id":"2001.10494","title":"Real-time Out-of-distribution Detection in Learning-Enabled Cyber-Physical Systems","created":"2020-01-28","cited_by_count":46},{"id":"1812.05389","title":"Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry","created":"2018-12-13","cited_by_count":38},{"id":"1911.09032","title":"Outside the Box: Abstraction-Based Monitoring of Neural Networks","created":"2019-11-20","cited_by_count":36},{"id":"1703.00978","title":"Compositional Falsification of Cyber-Physical Systems with Machine Learning Components","created":"2017-03-02","cited_by_count":32},{"id":"1910.01907","title":"Attacking Vision-based Perception in End-to-End Autonomous Driving Models","created":"2019-10-02","cited_by_count":31},{"id":"1908.00528","title":"Neural Simplex Architecture","created":"2019-08-01","cited_by_count":29},{"id":"2012.04567","title":"Bayesian Image Reconstruction using Deep Generative Models","created":"2020-12-08","cited_by_count":29},{"id":"1912.00805","title":"Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study","created":"2019-11-28","cited_by_count":27},{"id":"2106.04823","title":"Taxonomy of Machine Learning Safety: A Survey and Primer","created":"2021-06-09","cited_by_count":25},{"id":"2008.08221","title":"Machine Learning for Reliability Engineering and Safety Applications: Review of Current Status and Future Opportunities","created":"2020-08-19","cited_by_count":23},{"id":"2211.16158","title":"Out-Of-Distribution Detection Is Not All You Need","created":"2022-11-29","cited_by_count":23},{"id":"2003.01668","title":"Model Assertions for Monitoring and Improving ML Models","created":"2020-03-03","cited_by_count":22},{"id":"1810.10193","title":"Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving","created":"2018-10-24","cited_by_count":21},{"id":"2009.06429","title":"Into the Unknown: Active Monitoring of Neural Networks","created":"2020-09-14","cited_by_count":19},{"id":"1504.02763","title":"Performance measures for classification systems with rejection","created":"2015-04-10","cited_by_count":16},{"id":"1903.06391","title":"Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors","created":"2019-03-15","cited_by_count":14},{"id":"2110.01232","title":"Benchmarking Safety Monitors for Image Classifiers with Machine Learning","created":"2021-10-04","cited_by_count":14},{"id":"1810.08083","title":"Good Initializations of Variational Bayes for Deep Models","created":"2018-10-18","cited_by_count":11},{"id":"1810.12819","title":"Informed Democracy: Voting-based Novelty Detection for Action Recognition","created":"2018-10-30","cited_by_count":11},{"id":"2208.14660","title":"Unifying Evaluation of Machine Learning Safety Monitors","created":"2022-08-31","cited_by_count":10},{"id":"2003.10804","title":"Detecting Adversarial Examples in Learning-Enabled Cyber-Physical Systems using Variational Autoencoder for Regression","created":"2020-03-21","cited_by_count":8},{"id":"2101.02767","title":"Combining pretrained CNN feature extractors to enhance clustering of complex natural images","created":"2021-01-07","cited_by_count":7},{"id":"2111.12360","title":"Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach","created":"2021-11-24","cited_by_count":5},{"id":"2002.03339","title":"Input Validation for Neural Networks via Runtime Local Robustness Verification","created":"2020-02-09","cited_by_count":4},{"id":"2009.14178","title":"Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering","created":"2020-09-29","cited_by_count":4},{"id":"2103.15456","title":"Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions","created":"2021-03-29","cited_by_count":4},{"id":"1903.01028","title":"IVOA: Introspective Vision for Obstacle Avoidance","created":"2019-03-04","cited_by_count":3},{"id":"2106.13394","title":"Countering Adversarial Examples: Combining Input Transformation and Noisy Training","created":"2021-06-25","cited_by_count":2},{"id":"2107.11736","title":"WiP Abstract : Robust Out-of-distribution Motion Detection and Localization in Autonomous CPS","created":"2021-07-25","cited_by_count":2},{"id":"2206.00772","title":"On the reversibility of adversarial attacks","created":"2022-06-01","cited_by_count":1}],"next_cursor":null}