{"list":"other","total":62,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"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":"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":"2012.12877","title":"Training data-efficient image transformers & distillation through attention","created":"2020-12-23","cited_by_count":4586},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1703.01365","title":"Axiomatic Attribution for Deep Networks","created":"2017-03-04","cited_by_count":3384},{"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":"1904.02884","title":"Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks","created":"2019-04-05","cited_by_count":442},{"id":"2102.04906","title":"Dynamic Neural Networks: A Survey","created":"2021-02-09","cited_by_count":408},{"id":"1908.06281","title":"Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks","created":"2019-08-17","cited_by_count":355},{"id":"1804.08150","title":"Deep Learning in Spiking Neural Networks","created":"2018-04-22","cited_by_count":263},{"id":"2103.15571","title":"Enhancing the Transferability of Adversarial Attacks through Variance Tuning","created":"2021-03-29","cited_by_count":244},{"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":"2102.00436","title":"Admix: Enhancing the Transferability of Adversarial Attacks","created":"2021-01-31","cited_by_count":156},{"id":"1907.10823","title":"Enhancing Adversarial Example Transferability with an Intermediate Level Attack","created":"2019-07-23","cited_by_count":140},{"id":"2107.14185","title":"Feature Importance-aware Transferable Adversarial Attacks","created":"2021-07-29","cited_by_count":127},{"id":"2106.06984","title":"A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration","created":"2021-06-13","cited_by_count":117},{"id":"2010.05300","title":"Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification","created":"2020-10-11","cited_by_count":100},{"id":"2204.00008","title":"Improving Adversarial Transferability via Neuron Attribution-Based Attacks","created":"2022-03-31","cited_by_count":93},{"id":"2012.11207","title":"On Success and Simplicity: A Second Look at Transferable Targeted Attacks","created":"2020-12-21","cited_by_count":89},{"id":"2111.10752","title":"Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability","created":"2021-11-21","cited_by_count":84},{"id":"2106.15058","title":"Improving Transferability of Adversarial Patches on Face Recognition with Generative Models","created":"2021-06-29","cited_by_count":80},{"id":"2109.04176","title":"Towards Transferable Adversarial Attacks on Vision Transformers","created":"2021-09-09","cited_by_count":80},{"id":"2012.03528","title":"Backpropagating Linearly Improves Transferability of Adversarial Examples","created":"2020-12-07","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":"2106.04169","title":"On Improving Adversarial Transferability of Vision Transformers","created":"2021-06-08","cited_by_count":70},{"id":"2210.05968","title":"Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation","created":"2022-10-12","cited_by_count":68},{"id":"2004.14861","title":"Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability","created":"2020-04-29","cited_by_count":57},{"id":"2110.02797","title":"Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs","created":"2021-10-06","cited_by_count":57},{"id":"1909.04385","title":"FDA: Feature Disruptive Attack","created":"2019-09-10","cited_by_count":56},{"id":"2004.12519","title":"Transferable Perturbations of Deep Feature Distributions","created":"2020-04-27","cited_by_count":51},{"id":"2203.09123","title":"Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input","created":"2022-03-17","cited_by_count":50},{"id":"2111.10659","title":"Are Vision Transformers Robust to Patch Perturbations?","created":"2021-11-20","cited_by_count":39},{"id":"2207.13129","title":"LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity","created":"2022-07-26","cited_by_count":39},{"id":"2112.06011","title":"Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fitting","created":"2021-12-11","cited_by_count":37},{"id":"2010.03316","title":"Batch Normalization Increases Adversarial Vulnerability and Decreases Adversarial Transferability: A Non-Robust Feature Perspective","created":"2020-10-07","cited_by_count":35},{"id":"2205.13152","title":"Transferable Adversarial Attack based on Integrated Gradients","created":"2022-05-26","cited_by_count":35},{"id":"2207.11347","title":"An Impartial Take to the CNN vs Transformer Robustness Contest","created":"2022-07-22","cited_by_count":34},{"id":"2302.05086","title":"Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples","created":"2023-02-10","cited_by_count":32},{"id":"2207.10498","title":"Towards Efficient Adversarial Training on Vision Transformers","created":"2022-07-21","cited_by_count":30},{"id":"2204.00089","title":"Investigating Top-$k$ White-Box and Transferable Black-box Attack","created":"2022-03-30","cited_by_count":26},{"id":"1904.00979","title":"Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses","created":"2019-04-01","cited_by_count":23},{"id":"2112.14337","title":"Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently","created":"2021-12-29","cited_by_count":22},{"id":"1911.07968","title":"Improving the Robustness of Capsule Networks to Image Affine Transformations","created":"2019-11-18","cited_by_count":21},{"id":"2007.03838","title":"Making Adversarial Examples More Transferable and Indistinguishable","created":"2020-07-08","cited_by_count":19},{"id":"1811.09020","title":"Task-generalizable Adversarial Attack based on Perceptual Metric","created":"2018-11-22","cited_by_count":18},{"id":"1611.06179","title":"LOTS about Attacking Deep Features","created":"2016-11-18","cited_by_count":14},{"id":"2102.10055","title":"Effective and Efficient Vote Attack on Capsule Networks","created":"2021-02-19","cited_by_count":14},{"id":"2112.06658","title":"Learning to Learn Transferable Attack","created":"2021-12-10","cited_by_count":13},{"id":"2209.03358","title":"Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples","created":"2022-09-07","cited_by_count":13},{"id":"2103.15459","title":"Capsule Network is Not More Robust than Convolutional Network","created":"2021-03-29","cited_by_count":12},{"id":"2202.01832","title":"Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization","created":"2022-02-03","cited_by_count":8},{"id":"2205.10121","title":"Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration","created":"2022-05-06","cited_by_count":7},{"id":"2304.08479","title":"Towards Robust Prompts on Vision-Language Models","created":"2023-04-17","cited_by_count":7}],"next_cursor":null}