{"list":"other","total":59,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"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":"1512.00567","title":"Rethinking the Inception Architecture for Computer Vision","created":"2015-12-02","cited_by_count":5475},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1708.07747","title":"Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms","created":"2017-08-25","cited_by_count":4349},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"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":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"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":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"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":"1706.08498","title":"Spectrally-normalized margin bounds for neural networks","created":"2017-06-26","cited_by_count":686},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"id":"1803.06978","title":"Improving Transferability of Adversarial Examples with Input Diversity","created":"2018-03-19","cited_by_count":599},{"id":"2002.08347","title":"On Adaptive Attacks to Adversarial Example Defenses","created":"2020-02-19","cited_by_count":555},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1511.06452","title":"Deep Metric Learning via Lifted Structured Feature Embedding","created":"2015-11-19","cited_by_count":449},{"id":"1904.02884","title":"Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks","created":"2019-04-05","cited_by_count":442},{"id":"2003.08505","title":"A Metric Learning Reality Check","created":"2020-03-18","cited_by_count":321},{"id":"1812.03411","title":"Feature Denoising for Improving Adversarial Robustness","created":"2018-12-09","cited_by_count":314},{"id":"1704.08847","title":"Parseval Networks: Improving Robustness to Adversarial Examples","created":"2017-04-28","cited_by_count":293},{"id":"1706.07567","title":"Sampling Matters in Deep Embedding Learning","created":"2017-06-23","cited_by_count":285},{"id":"1705.09064","title":"MagNet: a Two-Pronged Defense against Adversarial Examples","created":"2017-05-25","cited_by_count":269},{"id":"1404.4661","title":"Learning Fine-grained Image Similarity with Deep Ranking","created":"2014-04-17","cited_by_count":190},{"id":"1904.04433","title":"Efficient Decision-based Black-box Adversarial Attacks on Face Recognition","created":"2019-04-09","cited_by_count":175},{"id":"1712.00673","title":"Towards Robust Neural Networks via Random Self-ensemble","created":"2017-12-02","cited_by_count":159},{"id":"1709.04114","title":"EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples","created":"2017-09-13","cited_by_count":155},{"id":"1907.02610","title":"Adversarial Robustness through Local Linearization","created":"2019-07-04","cited_by_count":146},{"id":"1907.10823","title":"Enhancing Adversarial Example Transferability with an Intermediate Level Attack","created":"2019-07-23","cited_by_count":140},{"id":"2002.08473","title":"Revisiting Training Strategies and Generalization Performance in Deep Metric Learning","created":"2020-02-19","cited_by_count":100},{"id":"1909.05040","title":"Sparse and Imperceivable Adversarial Attacks","created":"2019-09-11","cited_by_count":95},{"id":"1707.08945","title":"Robust Physical-World Attacks on Deep Learning Models","created":"2017-07-27","cited_by_count":72},{"id":"1801.08926","title":"Deflecting Adversarial Attacks with Pixel Deflection","created":"2018-01-26","cited_by_count":71},{"id":"1810.01279","title":"Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network","created":"2018-10-01","cited_by_count":69},{"id":"1811.07457","title":"Generalizable Adversarial Training via Spectral Normalization","created":"2018-11-19","cited_by_count":68},{"id":"1511.05122","title":"Adversarial Manipulation of Deep Representations","created":"2015-11-16","cited_by_count":56},{"id":"1808.03908","title":"Adversarial Personalized Ranking for Recommendation","created":"2018-08-12","cited_by_count":49},{"id":"1811.10716","title":"Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks","created":"2018-11-26","cited_by_count":48},{"id":"2004.04199","title":"Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking","created":"2020-04-08","cited_by_count":42},{"id":"1904.01160","title":"Curls & Whey: Boosting Black-Box Adversarial Attacks","created":"2019-04-02","cited_by_count":37},{"id":"1908.09327","title":"advPattern: Physical-World Attacks on Deep Person Re-Identification via Adversarially Transformable Patterns","created":"2019-08-25","cited_by_count":35},{"id":"1812.00552","title":"Universal Perturbation Attack Against Image Retrieval","created":"2018-12-03","cited_by_count":32},{"id":"1908.09163","title":"Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower","created":"2019-08-24","cited_by_count":26},{"id":"2104.09284","title":"LAFEAT: Piercing Through Adversarial Defenses with Latent Features","created":"2021-04-19","cited_by_count":24},{"id":"2103.02927","title":"QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval","created":"2021-03-04","cited_by_count":22},{"id":"1903.01612","title":"Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search","created":"2019-03-05","cited_by_count":19},{"id":"1806.04425","title":"Ranking Robustness Under Adversarial Document Manipulations","created":"2018-06-12","cited_by_count":15},{"id":"1911.07528","title":"Ladder Loss for Coherent Visual-Semantic Embedding","created":"2019-11-18","cited_by_count":15},{"id":"2002.11293","title":"Adversarial Ranking Attack and Defense","created":"2020-02-26","cited_by_count":14},{"id":"2002.11374","title":"Adversarial Attack on Deep Product Quantization Network for Image Retrieval","created":"2020-02-26","cited_by_count":13},{"id":"2103.05248","title":"Practical Relative Order Attack in Deep Ranking","created":"2021-03-09","cited_by_count":9},{"id":"1912.11236","title":"Ordered or Orderless: A Revisit for Video based Person Re-Identification","created":"2019-12-24","cited_by_count":7}],"next_cursor":null}