{"list":"other","total":54,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2105.05233","title":"Diffusion Models Beat GANs on Image Synthesis","created":"2021-05-11","cited_by_count":8255},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"1406.2283","title":"Depth Map Prediction from a Single Image using a Multi-Scale Deep Network","created":"2014-06-09","cited_by_count":1274},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1702.01135","title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","created":"2017-02-03","cited_by_count":744},{"id":"1711.00851","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","created":"2017-11-02","cited_by_count":567},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"1811.00866","title":"Efficient Neural Network Robustness Certification with General Activation Functions","created":"2018-11-02","cited_by_count":445},{"id":"1805.06605","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","created":"2018-05-17","cited_by_count":362},{"id":"1906.04584","title":"Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers","created":"2019-06-09","cited_by_count":330},{"id":"1801.09344","title":"Certified Defenses against Adversarial Examples","created":"2018-01-29","cited_by_count":306},{"id":"1801.02613","title":"Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality","created":"2018-01-08","cited_by_count":282},{"id":"1801.02610","title":"Generating Adversarial Examples with Adversarial Networks","created":"2018-01-08","cited_by_count":258},{"id":"1705.01320","title":"Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks","created":"2017-05-03","cited_by_count":242},{"id":"1903.06758","title":"Algorithms for Verifying Deep Neural Networks","created":"2019-03-15","cited_by_count":221},{"id":"1811.01057","title":"Semidefinite relaxations for certifying robustness to adversarial examples","created":"2018-11-02","cited_by_count":179},{"id":"2206.08367","title":"SHIFT: A Synthetic Driving Dataset for Continuous Multi-Task Domain Adaptation","created":"2022-06-16","cited_by_count":146},{"id":"2206.10550","title":"(Certified!!) Adversarial Robustness for Free!","created":"2022-06-21","cited_by_count":132},{"id":"1902.08722","title":"A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks","created":"2019-02-23","cited_by_count":125},{"id":"2202.10103","title":"Robustness and Accuracy Could Be Reconcilable by (Proper) Definition","created":"2022-02-21","cited_by_count":111},{"id":"2009.04131","title":"SoK: Certified Robustness for Deep Neural Networks","created":"2020-09-09","cited_by_count":105},{"id":"2208.05740","title":"General Cutting Planes for Bound-Propagation-Based Neural Network Verification","created":"2022-08-11","cited_by_count":103},{"id":"2102.11273","title":"On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness","created":"2021-02-22","cited_by_count":97},{"id":"1804.00499","title":"Semantic Adversarial Examples","created":"2018-03-16","cited_by_count":86},{"id":"2103.03638","title":"PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations","created":"2021-03-05","cited_by_count":77},{"id":"2006.04062","title":"Consistency Regularization for Certified Robustness of Smoothed Classifiers","created":"2020-06-07","cited_by_count":73},{"id":"2010.11645","title":"Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming","created":"2020-10-22","cited_by_count":66},{"id":"2206.09682","title":"SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles","created":"2022-06-20","cited_by_count":66},{"id":"2002.12463","title":"Certified Defense to Image Transformations via Randomized Smoothing","created":"2020-02-27","cited_by_count":51},{"id":"2002.12398","title":"TSS: Transformation-Specific Smoothing for Robustness Certification","created":"2020-02-27","cited_by_count":49},{"id":"1711.09115","title":"Geometric robustness of deep networks: analysis and improvement","created":"2017-11-24","cited_by_count":45},{"id":"2010.00573","title":"DASGIL: Domain Adaptation for Semantic and Geometric-aware Image-based Localization","created":"2020-10-01","cited_by_count":28},{"id":"2206.04310","title":"GSmooth: Certified Robustness against Semantic Transformations via Generalized Randomized Smoothing","created":"2022-06-09","cited_by_count":23},{"id":"2105.00373","title":"Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving","created":"2021-05-02","cited_by_count":22},{"id":"1712.01785","title":"Towards Practical Verification of Machine Learning: The Case of Computer Vision Systems","created":"2017-12-05","cited_by_count":19},{"id":"2011.14448","title":"Improved Handling of Motion Blur in Online Object Detection","created":"2020-11-29","cited_by_count":18},{"id":"2107.00996","title":"DeformRS: Certifying Input Deformations with Randomized Smoothing","created":"2021-07-02","cited_by_count":17},{"id":"2103.16652","title":"Robustness Certification for Point Cloud Models","created":"2021-03-30","cited_by_count":16},{"id":"1804.07045","title":"Semantic Adversarial Deep Learning","created":"2018-04-19","cited_by_count":15},{"id":"2207.11177","title":"Provable Defense Against Geometric Transformations","created":"2022-07-22","cited_by_count":14},{"id":"2307.15061","title":"The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation","created":"2023-07-27","cited_by_count":13},{"id":"1809.05845","title":"Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach","created":"2018-09-16","cited_by_count":12},{"id":"2009.09318","title":"Efficient Certification of Spatial Robustness","created":"2020-09-19","cited_by_count":11},{"id":"2201.12733","title":"TPC: Transformation-Specific Smoothing for Point Cloud Models","created":"2022-01-30","cited_by_count":10},{"id":"2209.05055","title":"CARE: Certifiably Robust Learning with Reasoning via Variational Inference","created":"2022-09-12","cited_by_count":10},{"id":"2011.04408","title":"SeasonDepth: Cross-Season Monocular Depth Prediction Dataset and Benchmark under Multiple Environments","created":"2020-11-09","cited_by_count":9},{"id":"1909.10184","title":"Retrieval-based Localization Based on Domain-invariant Feature Learning under Changing Environments","created":"2019-09-23","cited_by_count":7},{"id":"2009.07719","title":"Domain-invariant Similarity Activation Map Contrastive Learning for Retrieval-based Long-term Visual Localization","created":"2020-09-16","cited_by_count":3},{"id":"2210.04625","title":"Robustness Certification of Visual Perception Models via Camera Motion Smoothing","created":"2022-10-04","cited_by_count":3}],"next_cursor":null}