{"list":"other","total":71,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1905.11946","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","created":"2019-05-28","cited_by_count":6442},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"2201.03545","title":"A ConvNet for the 2020s","created":"2022-01-10","cited_by_count":3579},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1611.05431","title":"Aggregated Residual Transformations for Deep Neural Networks","created":"2016-11-16","cited_by_count":2546},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1901.08573","title":"Theoretically Principled Trade-off between Robustness and Accuracy","created":"2019-01-24","cited_by_count":1517},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1602.07360","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","created":"2016-02-24","cited_by_count":1060},{"id":"2003.13678","title":"Designing Network Design Spaces","created":"2020-03-30","cited_by_count":960},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1807.11626","title":"MnasNet: Platform-Aware Neural Architecture Search for Mobile","created":"2018-07-31","cited_by_count":943},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"1912.00049","title":"Square Attack: a query-efficient black-box adversarial attack via random search","created":"2019-11-29","cited_by_count":685},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1803.06978","title":"Improving Transferability of Adversarial Examples with Input Diversity","created":"2018-03-19","cited_by_count":599},{"id":"1611.02770","title":"Delving into Transferable Adversarial Examples and Black-box Attacks","created":"2016-11-08","cited_by_count":536},{"id":"1711.00117","title":"Countering Adversarial Images using Input Transformations","created":"2017-10-31","cited_by_count":520},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1712.04248","title":"Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models","created":"2017-12-12","cited_by_count":462},{"id":"1904.02884","title":"Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks","created":"2019-04-05","cited_by_count":442},{"id":"2106.14881","title":"Early Convolutions Help Transformers See Better","created":"2021-06-28","cited_by_count":396},{"id":"1907.02044","title":"Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack","created":"2019-07-03","cited_by_count":334},{"id":"1904.02144","title":"HopSkipJumpAttack: A Query-Efficient Decision-Based Attack","created":"2019-04-03","cited_by_count":333},{"id":"2007.08489","title":"Do Adversarially Robust ImageNet Models Transfer Better?","created":"2020-07-16","cited_by_count":316},{"id":"1905.07121","title":"Simple Black-box Adversarial Attacks","created":"2019-05-17","cited_by_count":251},{"id":"2103.15571","title":"Enhancing the Transferability of Adversarial Attacks through Variance Tuning","created":"2021-03-29","cited_by_count":244},{"id":"2002.11242","title":"Attacks Which Do Not Kill Training Make Adversarial Learning Stronger","created":"2020-02-26","cited_by_count":243},{"id":"2302.04638","title":"Better Diffusion Models Further Improve Adversarial Training","created":"2023-02-09","cited_by_count":208},{"id":"2002.05990","title":"Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets","created":"2020-02-14","cited_by_count":167},{"id":"2104.02610","title":"On the Robustness of Vision Transformers to Adversarial Examples","created":"2021-03-31","cited_by_count":167},{"id":"1704.03453","title":"The Space of Transferable Adversarial Examples","created":"2017-04-11","cited_by_count":158},{"id":"2010.01950","title":"Torchattacks: A PyTorch Repository for Adversarial Attacks","created":"2020-09-24","cited_by_count":158},{"id":"2102.00436","title":"Admix: Enhancing the Transferability of Adversarial Attacks","created":"2021-01-31","cited_by_count":156},{"id":"1909.10773","title":"Sign-OPT: A Query-Efficient Hard-label Adversarial Attack","created":"2019-09-24","cited_by_count":120},{"id":"2006.12792","title":"RayS: A Ray Searching Method for Hard-label Adversarial Attack","created":"2020-06-23","cited_by_count":96},{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85},{"id":"2303.01870","title":"Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models","created":"2023-03-03","cited_by_count":79},{"id":"2010.04055","title":"A Unified Approach to Interpreting and Boosting Adversarial Transferability","created":"2020-10-08","cited_by_count":71},{"id":"1812.03413","title":"Learning Transferable Adversarial Examples via Ghost Networks","created":"2018-12-09","cited_by_count":70},{"id":"1911.07140","title":"Black-Box Adversarial Attack with Transferable Model-based Embedding","created":"2019-11-17","cited_by_count":57},{"id":"2104.11470","title":"Random Noise Defense Against Query-Based Black-Box Attacks","created":"2021-04-23","cited_by_count":47},{"id":"2308.16258","title":"Robust Principles: Architectural Design Principles for Adversarially Robust CNNs","created":"2023-08-30","cited_by_count":46},{"id":"2207.13129","title":"LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity","created":"2022-07-26","cited_by_count":39},{"id":"2007.07435","title":"AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows","created":"2020-07-15","cited_by_count":36},{"id":"2106.02105","title":"A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks","created":"2021-06-03","cited_by_count":34},{"id":"2307.07873","title":"Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability","created":"2023-07-15","cited_by_count":32},{"id":"2006.14512","title":"Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability","created":"2020-06-25","cited_by_count":22},{"id":"2312.16979","title":"BlackboxBench: A Comprehensive Benchmark of Black-box Adversarial Attacks","created":"2023-12-28","cited_by_count":21},{"id":"2205.12134","title":"Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks","created":"2022-05-24","cited_by_count":19},{"id":"2102.03716","title":"SPADE: A Spectral Method for Black-Box Adversarial Robustness Evaluation","created":"2021-02-07","cited_by_count":16},{"id":"2402.02263","title":"MixedNUTS: Training-Free Accuracy-Robustness Balance via Nonlinearly Mixed Classifiers","created":"2024-02-03","cited_by_count":16},{"id":"2208.03610","title":"Blackbox Attacks via Surrogate Ensemble Search","created":"2022-08-07","cited_by_count":13},{"id":"2006.10876","title":"Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples","created":"2020-06-18","cited_by_count":11},{"id":"2206.09238","title":"On the Role of Generalization in Transferability of Adversarial Examples","created":"2022-06-18","cited_by_count":9},{"id":"2210.03297","title":"Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems","created":"2022-10-07","cited_by_count":5},{"id":"2311.06423","title":"Transferability Bound Theory: Exploring Relationship between Adversarial Transferability and Flatness","created":"2023-11-10","cited_by_count":5},{"id":"2210.11407","title":"Similarity of Neural Architectures using Adversarial Attack Transferability","created":"2022-10-20","cited_by_count":4},{"id":"2310.00567","title":"Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks","created":"2023-10-01","cited_by_count":4}],"next_cursor":null}